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index a6344aac8c09253b3b630fb776ae94478aa0275b..d28b13c93d247d7b4832f743f5108cd5694d4a30 100644
--- a/.gitattributes
+++ b/.gitattributes
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diff --git a/almeval/models/stepaudio/Dockerfile-vllm b/almeval/models/stepaudio/Dockerfile-vllm
new file mode 100644
index 0000000000000000000000000000000000000000..afa4605074a69d6b046e028f531b25f4d8c17897
--- /dev/null
+++ b/almeval/models/stepaudio/Dockerfile-vllm
@@ -0,0 +1,35 @@
+FROM nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04
+
+ENV TZ=Asia/Shanghai
+RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime \
+ && echo $TZ > /etc/timezone
+
+RUN apt-get update \
+ && apt-get install -y build-essential \
+ && apt-get install -y wget \
+ && apt-get install -y software-properties-common curl zip unzip git-lfs awscli libssl-dev openssh-server vim \
+ && apt-get install -y net-tools iputils-ping iproute2 \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN apt-get install --reinstall ca-certificates && update-ca-certificates \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN add-apt-repository -y 'ppa:deadsnakes/ppa' && apt update
+RUN apt install python3.10 python3.10-dev python3.10-distutils python3.10-venv -y \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN wget -qO- https://bootstrap.pypa.io/get-pip.py | python3.10
+RUN ln -s /usr/bin/python3.10 /usr/bin/python
+RUN pip uninstall -y Pillow && pip install pillow
+
+COPY requirements-vllm.txt /tmp/requirements.txt
+RUN pip3 install -r /tmp/requirements.txt
+# update vllm
+RUN VLLM_PYTHON_DIR=$(pip3 show vllm | grep Location | awk '{print $2}')/vllm \
+ && git clone -b add-step1-model https://github.com/stepfun-ai/vllm.git /tmp/vllm \
+ && cd /tmp/vllm/vllm \
+ && find . -name '*.py' -exec cp -v --parents -t $VLLM_PYTHON_DIR {} + \
+ && rm -rf /tmp/vllm
\ No newline at end of file
diff --git a/almeval/models/stepaudio/README_JP.md b/almeval/models/stepaudio/README_JP.md
new file mode 100644
index 0000000000000000000000000000000000000000..3550d6e433a6b12aef7ed9e82d35114e42ef4e32
--- /dev/null
+++ b/almeval/models/stepaudio/README_JP.md
@@ -0,0 +1,674 @@
+
+ 中文   |  English  |   日本語 
+
+
+
+# Step-Audio
+
+
+
+
+

+

+
+
+
+

+

+
+
+

+

+
+
+## 🔥🔥🔥 ニュース!!
+* 2025年2月17日: 👋 推論コードとモデルの重みをリリースしました。[Step-Audio-Chat](https://huggingface.co/stepfun-ai/Step-Audio-Chat), [Step-Audio-TTS-3B](https://huggingface.co/stepfun-ai/Step-Audio-TTS-3B) および [Step-Audio-Tokenizer](https://huggingface.co/stepfun-ai/Step-Audio-Tokenizer)。
+* 2025年2月17日: 👋 マルチターンオーディオベンチマーク [StepEval-Audio-360](https://huggingface.co/datasets/stepfun-ai/StepEval-Audio-360) をリリースしました。
+* 2025年2月17日: 👋 技術レポート [Step-Audio-Report](https://arxiv.org/abs/2502.11946) をリリースしました。
+
+## 目次
+
+1. [紹介](#1-紹介)
+2. [モデル概要](#2-モデル概要)
+3. [モデルのダウンロード](#3-モデルのダウンロード)
+4. [モデルの使用方法](#4-モデルの使用方法)
+5. [ベンチマーク](#5-ベンチマーク)
+6. [オンラインエンジン](#6-オンラインエンジン)
+7. [引用](#7-引用)
+
+## 1. 紹介
+
+Step-Audioは、音声理解と生成を統合した業界初の製品レベルのオープンソースリアルタイム音声対話システムであり、多言語対話(例:日本語、英語、中国語)、音声感情(例:喜び、悲しみ)、方言(例:関西弁、広東語)、音声速度および韻律スタイルの調整をサポートします。Step-Audioは、以下の4つの主要な技術革新を示しています:
+
+- **1300億パラメータのマルチモーダルモデル**:単一の統合モデルで、音声認識、意味理解、対話、音声クローン、音声生成を実行します。1300億パラメータのStep-Audio-Chatバリアントをオープンソース化しました。
+
+- **生成データエンジン**:従来のTTSが手動データ収集に依存することを排除し、1300億パラメータのマルチモーダルモデルを使用して高品質の音声を生成します。このデータを活用して、リソース効率の高いStep-Audio-TTS-3Bモデルをトレーニングし、制御可能な音声合成のための指示フォロー機能を強化しました。
+
+- **細かい音声制御**:指示ベースの制御設計を通じて、複数の感情(怒り、喜び、悲しみ)、方言(関西弁、広東語など)、および音声スタイル(ラップ、アカペラハミング)をサポートし、多様な音声生成ニーズに対応します。
+
+- **強化されたインテリジェンス**:ToolCallメカニズムの統合とロールプレイングの強化を通じて、エージェントの複雑なタスクにおけるパフォーマンスを向上させます。
+
+## 2. モデル概要
+Step-Audioでは、音声ストリームをトークン化するために、並列のセマンティック(16.7Hz、1024エントリのコードブック)および音響(25Hz、4096エントリのコードブック)トークナイザーを組み合わせたデュアルコードブックフレームワークを使用し、2:3の時間的インターリーブを行います。1300億パラメータのLLM基盤(Step-1)は、音声コンテキスト化継続的事前トレーニングおよびタスク固有の後トレーニングを通じて強化され、強力なクロスモーダル音声理解を実現します。フローマッチングとニューラルボコーダを組み合わせたハイブリッド音声デコーダを使用し、リアルタイムの波形生成を最適化します。推論パイプラインは、投機的応答生成(40%のコミット率)およびテキストベースのコンテキスト管理(14:1の圧縮率)を特徴とするストリーミング対応アーキテクチャを備えています。
+
+
+### 2.1 トークナイザー
+
+セマンティックトークナイザーと音響トークナイザーを効果的に統合するために、トークンレベルのインターリーブアプローチを実装しています。セマンティックトークナイザーは1024のコードブックサイズを使用し、音響トークナイザーはより大きな4096のコードブックサイズを使用して、より細かい音響の詳細をキャプチャします。異なるトークンレートを考慮して、2つのセマンティックトークンごとに3つの音響トークンをペアリングする2:3の時間的アライメント比を確立します。
+
+### 2.2 言語モデル
+
+Step-Audioの音声情報を効果的に処理し、正確な音声-テキストアライメントを実現するために、1300億パラメータの事前トレーニングされたテキストベースの大規模言語モデル(LLM)であるStep-1に基づいて、音声継続的事前トレーニングを実施しました。
+
+### 2.3 音声デコーダ
+Step-Audioの音声デコーダは、セマンティックおよび音響情報を含む離散音声トークンを、自然な音声を表す連続的な時間領域の波形に変換する重要な機能を果たします。デコーダアーキテクチャには、フローマッチングモデルとメルから波形へのボコーダが組み込まれています。生成された音声の明瞭度と自然さを最適化するために、音声デコーダはデュアルコードインターリーブアプローチを使用してトレーニングされ、生成プロセス全体でセマンティックおよび音響機能のシームレスな統合を確保します。
+
+### 2.4 リアルタイム推論パイプライン
+リアルタイムの対話を可能にするために、最適化された推論パイプラインを設計しました。その中心には、状態遷移を管理し、投機的応答生成を調整し、重要なサブシステム間のシームレスな調整を確保するコントローラーモジュールがあります。これらのサブシステムには、ユーザーの音声を検出する音声活動検出(VAD)、リアルタイムで音声を処理するストリーミングオーディオトークナイザー、応答を処理および生成するStep-Audio言語モデルおよび音声デコーダ、および会話の連続性を維持するコンテキストマネージャが含まれます。
+
+
+### 2.5 後トレーニングの詳細
+後トレーニングフェーズでは、自動音声認識(ASR)およびテキストから音声への変換(TTS)のタスク固有の監督付き微調整(SFT)を実施しました。音声入力テキスト出力(AQTA)タスクについては、多様な高品質データセットを使用してSFTを実施し、人間のフィードバックからの強化学習(RLHF)を組み合わせて応答品質を向上させ、感情表現、音声速度、方言、および韻律の細かい制御を可能にしました。
+
+
+
+## 3. モデルのダウンロード
+### 3.1 Huggingface
+| モデル | リンク |
+|-------|-------|
+| Step-Audio-Tokenizer | [🤗huggingface](https://huggingface.co/stepfun-ai/Step-Audio-Tokenizer) |
+| Step-Audio-Chat | [🤗huggingface](https://huggingface.co/stepfun-ai/Step-Audio-Chat) |
+| Step-Audio-TTS-3B | [🤗huggingface](https://huggingface.co/stepfun-ai/Step-Audio-TTS-3B) |
+
+### 3.2 Modelscope
+| モデル | リンク |
+|-------|-------|
+| Step-Audio-Tokenizer | [modelscope](https://modelscope.cn/models/stepfun-ai/Step-Audio-Tokenizer) |
+| Step-Audio-Chat | [modelscope](https://modelscope.cn/models/stepfun-ai/Step-Audio-Chat) |
+| Step-Audio-TTS-3B | [modelscope](https://modelscope.cn/models/stepfun-ai/Step-Audio-TTS-3B) |
+
+## 4. モデルの使用方法
+### 📜 4.1 要件
+次の表は、Step-Audioモデル(バッチサイズ=1)を実行するための要件を示しています:
+
+| モデル | 設定
(サンプル周波数) | GPU最小メモリ |
+|------------|--------------------------------|----------------|
+| Step-Audio-Tokenizer | 41.6Hz | 1.5GB |
+| Step-Audio-Chat | 41.6Hz | 265GB |
+| Step-Audio-TTS-3B | 41.6Hz | 8GB |
+
+* CUDAサポートのあるNVIDIA GPUが必要です。
+ * モデルは、4つのA800 80G GPUでテストされています。
+ * **推奨**:より良い生成品質のために、80GBメモリを持つ4つのA800/H800 GPUを使用することをお勧めします。
+* テストされたオペレーティングシステム:Linux
+
+### 🔧 4.2 依存関係とインストール
+- Python >= 3.10.0([Anaconda](https://www.anaconda.com/download/#linux)または[Miniconda](https://docs.conda.io/en/latest/miniconda.html)の使用を推奨)
+- [PyTorch >= 2.3-cu121](https://pytorch.org/)
+- [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads)
+
+```bash
+git clone https://github.com/stepfun-ai/Step-Audio.git
+conda create -n stepaudio python=3.10
+conda activate stepaudio
+
+cd Step-Audio
+pip install -r requirements.txt
+
+git lfs install
+git clone https://huggingface.co/stepfun-ai/Step-Audio-Tokenizer
+git clone https://huggingface.co/stepfun-ai/Step-Audio-Chat
+git clone https://huggingface.co/stepfun-ai/Step-Audio-TTS-3B
+
+```
+
+モデルをダウンロードした後、where_you_download_dirは次の構造を持つ必要があります:
+```
+where_you_download_dir
+├── Step-Audio-Tokenizer
+├── Step-Audio-Chat
+├── Step-Audio-TTS-3B
+```
+
+#### Docker 実行環境
+
+dockerを使用してStep-Audioの実行に必要な環境を作成します
+
+```bash
+# Dockerイメージのビルド
+docker build . -t step-audio
+
+# Dockerコンテナの実行
+docker run --rm -ti --gpus all \
+ -v /your/code/path:/app -v /your/model/path:/model \
+ -p 7860:7860 \
+ step-audio \
+ -- bash
+
+# vLLM Dockerイメージのビルド
+docker build -f Dockerfile-vllm -t step-audio-vllm .
+
+# vLLM Dockerコンテナの実行
+docker run --rm -ti --gpus all \
+ -v /your/code/path:/app -v /your/model/path:/model \
+ -p 7860:7860 \
+ -p 8000:8000 \
+ step-audio-vllm \
+ -- bash
+```
+
+
+### 🚀 4.3 推論スクリプト
+#### オフライン推論
+エンドツーエンドの音声/テキスト入力と音声/テキスト出力で推論を行います。
+```bash
+python offline_inference.py --model-path where_you_download_dir
+```
+#### TTS推論
+デフォルトのスピーカーを使用してTTSを推論するか、新しいスピーカーでクローンを作成します
+```bash
+python tts_inference.py --model-path where_you_download_dir --output-path where_you_save_audio_dir --synthesis-type use_tts_or_clone
+```
+クローンモードには、次の形式のスピーカー情報辞書が必要です:
+```bash
+{
+ "speaker": "speaker id",
+ "prompt_text": "content of prompt wav",
+ "wav_path": "prompt wav path"
+}
+```
+
+#### Webデモの起動
+オンライン推論のためにローカルサーバーを起動します。
+4つのGPUが利用可能で、すべてのモデルをダウンロード済みであると仮定します。
+
+```bash
+# Step-Audio-Chat デモ
+python app.py --model-path where_you_download_dir
+
+# Step-Audio-TTS-3B デモ
+python tts_app.py --model-path where_you_download_dir
+
+```
+
+#### vLLMを用いた対話モデル推論(推奨)
+Step-Audio-Chatは130Bパラメータの大規模言語モデルであり、テンソル並列処理をサポートするvLLMを使用した推論を推奨します。
+ * vLLMはTokenizerおよびTTSをロードしないため、音声入力による推論には対応していません
+
+現在の公式vLLMはStep 1モデルアーキテクチャに対応していないため、当社の[開発ブランチ](https://github.com/stepfun-ai/vllm/tree/add-step1-model)を使用したローカルインストールを推奨します。
+
+本モデルのAttentionメカニズムはALIBIの変種実装を採用しているため、公式flash attentionライブラリとの互換性がありません。[Step-Audio-Chat](https://huggingface.co/stepfun-ai/Step-Audio-Chat/tree/main/lib)リポジトリにカスタム版flash attentionライブラリを提供しています。モデル実行前に必ず環境変数へカスタムライブラリのパスを追加してください。
+
+```bash
+export OPTIMUS_LIB_PATH=where_you_download_dir/Step-Audio-Chat/lib
+
+vllm serve where_you_download_dir/Step-Audio-Chat --dtype auto -tp $tp --served-model-name step-audio-chat --trust-remote-code
+
+# vLLMチャットの呼び出し例
+python call_vllm_chat.py
+```
+
+## 5. ベンチマーク
+
+### 5.1 ASR結果の比較
+
+
+
+
+ |
+ 隠れた特徴モデリング |
+ 離散音声トークンモデリング |
+
+
+ |
+ Whisper Large-v3 |
+ Qwen2-Audio |
+ MinMo |
+ LUCY |
+ Moshi |
+ GLM-4-voice Base |
+ GLM-4-voice Chat |
+ Step-Audio Pretrain |
+ Step-Audio-Chat |
+
+
+
+
+ | Aishell-1 |
+ 5.14 |
+ 1.53 |
+ - |
+ 2.4 |
+ - |
+ 2.46 |
+ 226.47 |
+ 0.87 |
+ 1.95 |
+
+
+ | Aishell-2 ios |
+ 4.76 |
+ 3.06 |
+ 2.69 |
+ - |
+ - |
+ - |
+ 211.3 |
+ 2.91 |
+ 3.57 |
+
+
+ | Wenetspeech test-net |
+ 9.68 |
+ 7.72 |
+ 6.64 |
+ 8.78 |
+ - |
+ - |
+ 146.05 |
+ 7.62 |
+ 8.75 |
+
+
+ | Wenet test-meeting |
+ 18.54 |
+ 8.4 |
+ 7.6 |
+ 10.42 |
+ - |
+ - |
+ 140.82 |
+ 7.78 |
+ 9.52 |
+
+
+ | Librispeech test-clean |
+ 1.9 |
+ 1.6 |
+ 1.6 |
+ 3.36 |
+ 5.7 |
+ 2.82 |
+ 75.39 |
+ 2.36 |
+ 3.11 |
+
+
+ | Librispeech test-other |
+ 3.65 |
+ 3.6 |
+ 3.82 |
+ 8.05 |
+ - |
+ 7.66 |
+ 80.3 |
+ 6.32 |
+ 8.44 |
+
+
+ | AVG |
+ 7.28 |
+ 4.32 |
+ - |
+ - |
+ - |
+ - |
+ 146.74 |
+ 4.64 |
+ 5.89 |
+
+
+
+
+### 5.2 TTS
+#### 5.2.1 GLM-4-VoiceとMinMoのコンテンツ一貫性(CER/WER)のパフォーマンス比較。
+
+
+
+
+ | モデル |
+ test-zh |
+ test-en |
+
+
+ | CER (%) ↓ |
+ WER (%) ↓ |
+
+
+
+
+ | GLM-4-Voice |
+ 2.19 |
+ 2.91 |
+
+
+ | MinMo |
+ 2.48 |
+ 2.90 |
+
+
+ | Step-Audio |
+ 1.53 |
+ 2.71 |
+
+
+
+
+#### 5.2.2 SEEDテストセットでのTTSモデルの結果。
+* StepAudio-TTS-3B-Singleは、デュアルコードブックバックボーンとシングルコードブックボコーダの組み合わせを示します。
+
+
+
+
+ | モデル |
+ test-zh |
+ test-en |
+
+
+ | CER (%) ↓ |
+ SS ↑ |
+ WER (%) ↓ |
+ SS ↑ |
+
+
+
+
+ | FireRedTTS |
+ 1.51 |
+ 0.630 |
+ 3.82 |
+ 0.460 |
+
+
+ | MaskGCT |
+ 2.27 |
+ 0.774 |
+ 2.62 |
+ 0.774 |
+
+
+ | CosyVoice |
+ 3.63 |
+ 0.775 |
+ 4.29 |
+ 0.699 |
+
+
+ | CosyVoice 2 |
+ 1.45 |
+ 0.806 |
+ 2.57 |
+ 0.736 |
+
+
+ | CosyVoice 2-S |
+ 1.45 |
+ 0.812 |
+ 2.38 |
+ 0.743 |
+
+
+ | Step-Audio-TTS-3B-Single |
+ 1.37 |
+ 0.802 |
+ 2.52 |
+ 0.704 |
+
+
+ | Step-Audio-TTS-3B |
+ 1.31 |
+ 0.733 |
+ 2.31 |
+ 0.660 |
+
+
+ | Step-Audio-TTS |
+ 1.17 |
+ 0.73 |
+ 2.0 |
+ 0.660 |
+
+
+
+
+#### 5.2.3 デュアルコードブック再合成とCosyVoiceのパフォーマンス比較。
+
+
+
+
+ | トークン |
+ test-zh |
+ test-en |
+
+
+ | CER (%) ↓ |
+ SS ↑ |
+ WER (%) ↓ |
+ SS ↑ |
+
+
+
+
+ | Groundtruth |
+ 0.972 |
+ - |
+ 2.156 |
+ - |
+
+
+ | CosyVoice |
+ 2.857 |
+ 0.849 |
+ 4.519 |
+ 0.807 |
+
+
+ | Step-Audio-TTS-3B |
+ 2.192 |
+ 0.784 |
+ 3.585 |
+ 0.742 |
+
+
+
+
+### 5.3 AQTAチャット
+[**StepEval-Audio-360**](https://huggingface.co/datasets/stepfun-ai/StepEval-Audio-360) を新しいベンチマークとしてリリースしました。これは、実際のユーザーからの137のマルチターンの日本語プロンプトで構成されており、生成された応答の品質を次の次元で評価するように設計されています:音声指示のフォロー、音声理解、論理的推論、ロールプレイング、創造性、歌唱、言語能力、音声感情制御、ゲーム。
+
+#### 5.3.1 StepEval-Audio-360
+
+#### LLM評価指標(GPT-4o)
+
+ StepEval-Audio-360での音声チャットの基本機能の比較。
+
+
+ | モデル |
+ 事実性(% ↑) |
+ 関連性(% ↑) |
+ チャットスコア ↑ |
+
+
+
+
+ | GLM4-Voice |
+ 54.7 |
+ 66.4 |
+ 3.49 |
+
+
+ | Qwen2-Audio |
+ 22.6 |
+ 26.3 |
+ 2.27 |
+
+
+ | Moshi* |
+ 1.0 |
+ 0 |
+ 1.49 |
+
+
+ | Step-Audio-Chat |
+ 66.4 |
+ 75.2 |
+ 4.11 |
+
+
+
+
+* 注:Moshiは「\*」でマークされており、参考として考慮する必要があります。
+
+#### レーダーチャート(人間の評価)
+
+
+#### 5.3.2 公開テストセット
+
+
+
+
+ | モデル |
+ Llama Question |
+ Web Questions |
+ TriviaQA* |
+ ComplexBench |
+ HSK-6 |
+
+
+
+
+ | GLM4-Voice |
+ 64.7 |
+ 32.2 |
+ 39.1 |
+ 66.0 |
+ 74.0 |
+
+
+ | Moshi |
+ 62.3 |
+ 26.6 |
+ 22.8 |
+ - |
+ - |
+
+
+ | Freeze-Omni |
+ 72.0 |
+ 44.7 |
+ 53.9 |
+ - |
+ - |
+
+
+ | LUCY |
+ 59.7 |
+ 29.3 |
+ 27.0 |
+ - |
+ - |
+
+
+ | MinMo |
+ 78.9 |
+ 55.0 |
+ 48.3 |
+ - |
+ - |
+
+
+ | Qwen2-Audio |
+ 52.0 |
+ 27.0 |
+ 37.3 |
+ 54.0 |
+ - |
+
+
+ | Step-Audio-Chat |
+ 81.0 |
+ 75.1 |
+ 58.0 |
+ 74.0 |
+ 86.0 |
+
+
+
+
+* 注:TriviaQAデータセットで「\*」でマークされた結果は参考として考慮されます。
+
+#### 5.3.3 音声指示のフォロー
+
+
+
+ | カテゴリ |
+ 指示のフォロー |
+ 音声品質 |
+
+
+ | GLM-4-Voice |
+ Step-Audio |
+ GLM-4-Voice |
+ Step-Audio |
+
+
+
+
+ | 言語 |
+ 1.9 |
+ 3.8 |
+ 2.9 |
+ 3.3 |
+
+
+ | ロールプレイング |
+ 3.8 |
+ 4.2 |
+ 3.2 |
+ 3.6 |
+
+
+ | 歌唱 / ラップ |
+ 2.1 |
+ 2.4 |
+ 2.4 |
+ 4 |
+
+
+ | 音声制御 |
+ 3.6 |
+ 4.4 |
+ 3.3 |
+ 4.1 |
+
+
+
+
+## 6. オンラインエンジン
+Step-Audioのオンラインバージョンは、[跃问](https://yuewen.cn)のアプリバージョンからアクセスでき、いくつかの印象的な例も見つけることができます。
+
+
+
+## 7. 例
+### 音声クローン
+| 役割 | プロンプト音声 | クローン音声 |
+|:-------:|:-------:|:-------:|
+|于谦| [google drive](https://drive.google.com/file/d/1N9EJypafFwmeL0R152GoL_CVGbYn1_9A/preview)
[audio file](https://github.com/stepfun-ai/Step-Audio/tree/main/examples/prompt_wav_yuqian.wav)|[google drive](https://drive.google.com/file/d/1Zs_1QrCUuoSqtUSdn2ENIor-k5baQdDV/preview)
[audio file](https://github.com/stepfun-ai/Step-Audio/tree/main/examples/clone_wav_yuqian.wav)|
+|李雪琴| [google drive](https://drive.google.com/file/d/15SkZ29hksELYi1NDOxYOPu-kRTLSyke_/preview)
[audio file](https://github.com/stepfun-ai/Step-Audio/tree/main/examples/prompt_wav_lixueqin.wav)|[google drive](https://drive.google.com/file/d/11Le4qMqL2DmWpf7RFRpKUXERIR9TtKC0/preview)
[audio file](https://github.com/stepfun-ai/Step-Audio/tree/main/examples/clone_wav_lixueqin.wav)|
+
+### 速度制御
+| プロンプト | 応答 |
+|:-------:|:-------:|
+|Human: 早口言葉を言ってください
Assistant: すもももももももものうち
Human: もっと早く言えますか?|[google drive](https://drive.google.com/file/d/1mAH-NRrOVZo4tv6gdAZkyJg8kRuTNNGC/preview)
[audio file](https://github.com/stepfun-ai/Step-Audio/tree/main/examples/speed_control1.wav)|
+|Human: 早口言葉を言ってください
Assistant: すもももももももものうち
Human: もっと早く言えますか?
Assistant: すもももももももものうち
Human: もっとゆっくり言ってください。|[google drive](https://drive.google.com/file/d/1FhRnKo8uGrtO-cWg4qkrg8iDoNRbtqSX/preview)
[audio file](https://github.com/stepfun-ai/Step-Audio/tree/main/examples/speed_control2.wav)|
+
+### 高EQ(感情制御 & トーン制御)
+| プロンプト | 応答 |
+|:-------:|:-------:|
+|Human: もっとかわいく話してみてください。|[google drive](https://drive.google.com/file/d/19IROE6_6h2UQVNniCmDTnrhxKRMOFHq3/preview)
[audio file](https://github.com/stepfun-ai/Step-Audio/tree/main/examples/tone_control.wav)|
+|Human: どうしよう、人生がうまくいかない。|[google drive](https://drive.google.com/file/d/1JlLbOlzmdrokVdxtwy1S8eeWqsZR2Vmc/preview)
[audio file](https://github.com/stepfun-ai/Step-Audio/tree/main/examples/emotional_control1.wav)|
+|Human: すごいですね。|[google drive](https://drive.google.com/file/d/19ga1RpguDP5r0Xfl1r5GY1J-kzbmHvJb/preview)
[audio file](https://github.com/stepfun-ai/Step-Audio/tree/main/examples/emotional_control2.wav)|
+
+### 多言語(例:日本語、英語、中国語)
+| プロンプト | 応答 |
+|:-------:|:-------:|
+|Human: "It's raining cats and dogs" ってどういう意味ですか?
Assistant: "It's raining cats and dogs" というのは、非常に激しい雨が降っていることを意味します。実際に猫や犬が空から降ってくるわけではありません!これは激しい雨を表現するための面白い言い方です。|[google drive](https://drive.google.com/file/d/1LEIvdR5ANMzWX8GOTqUPTNrynNS1xx--/preview)
[audio file](https://github.com
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diff --git a/almeval/models/stepaudio/examples/multilingual_singing.wav b/almeval/models/stepaudio/examples/multilingual_singing.wav
new file mode 100644
index 0000000000000000000000000000000000000000..c1b61b5b1b86c44a4ba4f8a59ae0f9f21eb1faf5
--- /dev/null
+++ b/almeval/models/stepaudio/examples/multilingual_singing.wav
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:58a5d3ee4067654abca8a6ca1afd6b9b83dfeadab2f40b112f203a4872bbe850
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diff --git a/almeval/models/stepaudio/examples/prompt_wav_lixueqin.wav b/almeval/models/stepaudio/examples/prompt_wav_lixueqin.wav
new file mode 100644
index 0000000000000000000000000000000000000000..b56cc42a06e6e15fb2395f7ea2da0f54d1fea882
--- /dev/null
+++ b/almeval/models/stepaudio/examples/prompt_wav_lixueqin.wav
@@ -0,0 +1,3 @@
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diff --git a/almeval/models/stepaudio/examples/prompt_wav_yuqian.wav b/almeval/models/stepaudio/examples/prompt_wav_yuqian.wav
new file mode 100644
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--- /dev/null
+++ b/almeval/models/stepaudio/examples/prompt_wav_yuqian.wav
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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new file mode 100644
index 0000000000000000000000000000000000000000..5737c0785a9cecc7830d410583215a07d79fa3d7
--- /dev/null
+++ b/almeval/models/stepaudio/examples/prompt_wav_zhaobenshan.wav
@@ -0,0 +1,3 @@
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diff --git a/almeval/models/stepaudio/examples/rap.wav b/almeval/models/stepaudio/examples/rap.wav
new file mode 100644
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--- /dev/null
+++ b/almeval/models/stepaudio/examples/rap.wav
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diff --git a/almeval/models/stepaudio/examples/singing.wav b/almeval/models/stepaudio/examples/singing.wav
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--- /dev/null
+++ b/almeval/models/stepaudio/examples/singing.wav
@@ -0,0 +1,3 @@
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diff --git a/almeval/models/stepaudio/examples/speed_control1.wav b/almeval/models/stepaudio/examples/speed_control1.wav
new file mode 100644
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--- /dev/null
+++ b/almeval/models/stepaudio/examples/speed_control1.wav
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diff --git a/almeval/models/stepaudio/examples/speed_control2.wav b/almeval/models/stepaudio/examples/speed_control2.wav
new file mode 100644
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--- /dev/null
+++ b/almeval/models/stepaudio/examples/speed_control2.wav
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diff --git a/almeval/models/stepaudio/examples/tone_control.wav b/almeval/models/stepaudio/examples/tone_control.wav
new file mode 100644
index 0000000000000000000000000000000000000000..37eadc79a4b7b86aaf526ceef6cc3a06d9741598
--- /dev/null
+++ b/almeval/models/stepaudio/examples/tone_control.wav
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/almeval/models/stepaudio/funasr_detach/__init__.py b/almeval/models/stepaudio/funasr_detach/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a85dbca3c32b291b206a49e60559069c8c4dadc
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/__init__.py
@@ -0,0 +1,38 @@
+"""Initialize funasr package."""
+
+import os
+import pkgutil
+import importlib
+
+dirname = os.path.dirname(__file__)
+version_file = os.path.join(dirname, "version.txt")
+with open(version_file, "r") as f:
+ __version__ = f.read().strip()
+
+
+import importlib
+import pkgutil
+
+
+def import_submodules(package, recursive=True):
+ if isinstance(package, str):
+ package = importlib.import_module(package)
+ results = {}
+ for loader, name, is_pkg in pkgutil.walk_packages(
+ package.__path__, package.__name__ + "."
+ ):
+ try:
+ results[name] = importlib.import_module(name)
+ except Exception as e:
+ # 如果想要看到导入错误的具体信息,可以取消注释下面的行
+ # print(f"Failed to import {name}: {e}")
+ pass
+ if recursive and is_pkg:
+ results.update(import_submodules(name))
+ return results
+
+
+import_submodules(__name__)
+
+from funasr_detach.auto.auto_model import AutoModel
+from funasr_detach.auto.auto_frontend import AutoFrontend
diff --git a/almeval/models/stepaudio/funasr_detach/auto/__init__.py b/almeval/models/stepaudio/funasr_detach/auto/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/almeval/models/stepaudio/funasr_detach/auto/auto_frontend.py b/almeval/models/stepaudio/funasr_detach/auto/auto_frontend.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bba442373cb0609df00b7d3bda6db3a338b9723
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/auto/auto_frontend.py
@@ -0,0 +1,90 @@
+import time
+import logging
+from tqdm import tqdm
+
+from funasr_detach.register import tables
+from funasr_detach.download.download_from_hub import download_model
+from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank
+from funasr_detach.auto.auto_model import prepare_data_iterator
+from funasr_detach.auto.auto_model import prepare_data_iterator
+
+
+class AutoFrontend:
+ def __init__(self, **kwargs):
+ assert "model" in kwargs
+ if "model_conf" not in kwargs:
+ logging.info(
+ "download models from model hub: {}".format(
+ kwargs.get("model_hub", "ms")
+ )
+ )
+ kwargs = download_model(**kwargs)
+
+ # build frontend
+ frontend = kwargs.get("frontend", None)
+ if frontend is not None:
+ frontend_class = tables.frontend_classes.get(frontend)
+ frontend = frontend_class(**kwargs["frontend_conf"])
+
+ self.frontend = frontend
+ if "frontend" in kwargs:
+ del kwargs["frontend"]
+ self.kwargs = kwargs
+
+ def __call__(self, input, input_len=None, kwargs=None, **cfg):
+
+ kwargs = self.kwargs if kwargs is None else kwargs
+ kwargs.update(cfg)
+
+ key_list, data_list = prepare_data_iterator(input, input_len=input_len)
+ batch_size = kwargs.get("batch_size", 1)
+ device = kwargs.get("device", "cpu")
+ if device == "cpu":
+ batch_size = 1
+
+ meta_data = {}
+
+ result_list = []
+ num_samples = len(data_list)
+ pbar = tqdm(colour="blue", total=num_samples + 1, dynamic_ncols=True)
+
+ time0 = time.perf_counter()
+ for beg_idx in range(0, num_samples, batch_size):
+ end_idx = min(num_samples, beg_idx + batch_size)
+ data_batch = data_list[beg_idx:end_idx]
+ key_batch = key_list[beg_idx:end_idx]
+
+ # extract fbank feats
+ time1 = time.perf_counter()
+ audio_sample_list = load_audio_text_image_video(
+ data_batch, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000)
+ )
+ time2 = time.perf_counter()
+ meta_data["load_data"] = f"{time2 - time1:0.3f}"
+ speech, speech_lengths = extract_fbank(
+ audio_sample_list,
+ data_type=kwargs.get("data_type", "sound"),
+ frontend=self.frontend,
+ **kwargs,
+ )
+ time3 = time.perf_counter()
+ meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+ meta_data["batch_data_time"] = (
+ speech_lengths.sum().item()
+ * self.frontend.frame_shift
+ * self.frontend.lfr_n
+ / 1000
+ )
+
+ speech.to(device=device), speech_lengths.to(device=device)
+ batch = {"input": speech, "input_len": speech_lengths, "key": key_batch}
+ result_list.append(batch)
+
+ pbar.update(1)
+ description = f"{meta_data}, "
+ pbar.set_description(description)
+
+ time_end = time.perf_counter()
+ pbar.set_description(f"time escaped total: {time_end - time0:0.3f}")
+
+ return result_list
diff --git a/almeval/models/stepaudio/funasr_detach/auto/auto_model.py b/almeval/models/stepaudio/funasr_detach/auto/auto_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..9315da8bfff1716377e9400e104c667fce743cfb
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/auto/auto_model.py
@@ -0,0 +1,573 @@
+import json
+import time
+import copy
+import torch
+import random
+import string
+import logging
+import os.path
+import numpy as np
+from tqdm import tqdm
+
+from funasr_detach.register import tables
+from funasr_detach.utils.load_utils import load_bytes
+from funasr_detach.download.file import download_from_url
+from funasr_detach.download.download_from_hub import download_model
+from funasr_detach.utils.vad_utils import slice_padding_audio_samples
+from funasr_detach.train_utils.set_all_random_seed import set_all_random_seed
+from funasr_detach.train_utils.load_pretrained_model import load_pretrained_model
+from funasr_detach.utils.load_utils import load_audio_text_image_video
+from funasr_detach.utils.timestamp_tools import timestamp_sentence
+from funasr_detach.models.campplus.utils import sv_chunk, postprocess, distribute_spk
+
+try:
+ from funasr_detach.models.campplus.cluster_backend import ClusterBackend
+except:
+ print("If you want to use the speaker diarization, please `pip install hdbscan`")
+
+
+def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
+ """
+
+ :param input:
+ :param input_len:
+ :param data_type:
+ :param frontend:
+ :return:
+ """
+ data_list = []
+ key_list = []
+ filelist = [".scp", ".txt", ".json", ".jsonl"]
+
+ chars = string.ascii_letters + string.digits
+ if isinstance(data_in, str) and data_in.startswith("http"): # url
+ data_in = download_from_url(data_in)
+ if isinstance(data_in, str) and os.path.exists(
+ data_in
+ ): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
+ _, file_extension = os.path.splitext(data_in)
+ file_extension = file_extension.lower()
+ if file_extension in filelist: # filelist: wav.scp, file.jsonl;text.txt;
+ with open(data_in, encoding="utf-8") as fin:
+ for line in fin:
+ key = "rand_key_" + "".join(random.choice(chars) for _ in range(13))
+ if data_in.endswith(
+ ".jsonl"
+ ): # file.jsonl: json.dumps({"source": data})
+ lines = json.loads(line.strip())
+ data = lines["source"]
+ key = data["key"] if "key" in data else key
+ else: # filelist, wav.scp, text.txt: id \t data or data
+ lines = line.strip().split(maxsplit=1)
+ data = lines[1] if len(lines) > 1 else lines[0]
+ key = lines[0] if len(lines) > 1 else key
+
+ data_list.append(data)
+ key_list.append(key)
+ else:
+ key = "rand_key_" + "".join(random.choice(chars) for _ in range(13))
+ data_list = [data_in]
+ key_list = [key]
+ elif isinstance(data_in, (list, tuple)):
+ if data_type is not None and isinstance(
+ data_type, (list, tuple)
+ ): # mutiple inputs
+ data_list_tmp = []
+ for data_in_i, data_type_i in zip(data_in, data_type):
+ key_list, data_list_i = prepare_data_iterator(
+ data_in=data_in_i, data_type=data_type_i
+ )
+ data_list_tmp.append(data_list_i)
+ data_list = []
+ for item in zip(*data_list_tmp):
+ data_list.append(item)
+ else:
+ # [audio sample point, fbank, text]
+ data_list = data_in
+ key_list = [
+ "rand_key_" + "".join(random.choice(chars) for _ in range(13))
+ for _ in range(len(data_in))
+ ]
+ else: # raw text; audio sample point, fbank; bytes
+ if isinstance(data_in, bytes): # audio bytes
+ data_in = load_bytes(data_in)
+ if key is None:
+ key = "rand_key_" + "".join(random.choice(chars) for _ in range(13))
+ data_list = [data_in]
+ key_list = [key]
+
+ return key_list, data_list
+
+
+class AutoModel:
+
+ def __init__(self, **kwargs):
+ if not kwargs.get("disable_log", False):
+ tables.print()
+
+ model, kwargs = self.build_model(**kwargs)
+
+ # if vad_model is not None, build vad model else None
+ vad_model = kwargs.get("vad_model", None)
+ vad_kwargs = kwargs.get("vad_model_revision", None)
+ if vad_model is not None:
+ logging.info("Building VAD model.")
+ vad_kwargs = {
+ "model": vad_model,
+ "model_revision": vad_kwargs,
+ "device": kwargs["device"],
+ }
+ vad_model, vad_kwargs = self.build_model(**vad_kwargs)
+
+ # if punc_model is not None, build punc model else None
+ punc_model = kwargs.get("punc_model", None)
+ punc_kwargs = kwargs.get("punc_model_revision", None)
+ if punc_model is not None:
+ logging.info("Building punc model.")
+ punc_kwargs = {
+ "model": punc_model,
+ "model_revision": punc_kwargs,
+ "device": kwargs["device"],
+ }
+ punc_model, punc_kwargs = self.build_model(**punc_kwargs)
+
+ # if spk_model is not None, build spk model else None
+ spk_model = kwargs.get("spk_model", None)
+ spk_kwargs = kwargs.get("spk_model_revision", None)
+ if spk_model is not None:
+ logging.info("Building SPK model.")
+ spk_kwargs = {
+ "model": spk_model,
+ "model_revision": spk_kwargs,
+ "device": kwargs["device"],
+ }
+ spk_model, spk_kwargs = self.build_model(**spk_kwargs)
+ self.cb_model = ClusterBackend().to(kwargs["device"])
+ spk_mode = kwargs.get("spk_mode", "punc_segment")
+ if spk_mode not in ["default", "vad_segment", "punc_segment"]:
+ logging.error(
+ "spk_mode should be one of default, vad_segment and punc_segment."
+ )
+ self.spk_mode = spk_mode
+
+ self.kwargs = kwargs
+ self.model = model
+ self.vad_model = vad_model
+ self.vad_kwargs = vad_kwargs
+ self.punc_model = punc_model
+ self.punc_kwargs = punc_kwargs
+ self.spk_model = spk_model
+ self.spk_kwargs = spk_kwargs
+ self.model_path = kwargs.get("model_path")
+
+ def build_model(self, **kwargs):
+ assert "model" in kwargs
+ if "model_conf" not in kwargs:
+ logging.info(
+ "download models from model hub: {}".format(
+ kwargs.get("model_hub", "ms")
+ )
+ )
+ kwargs = download_model(**kwargs)
+
+ set_all_random_seed(kwargs.get("seed", 0))
+
+ device = kwargs.get("device", "cuda")
+ if not torch.cuda.is_available() or kwargs.get("ngpu", 1) == 0:
+ device = "cpu"
+ kwargs["batch_size"] = 1
+ kwargs["device"] = device
+
+ if kwargs.get("ncpu", None):
+ torch.set_num_threads(kwargs.get("ncpu"))
+
+ # build tokenizer
+ tokenizer = kwargs.get("tokenizer", None)
+ if tokenizer is not None:
+ tokenizer_class = tables.tokenizer_classes.get(tokenizer)
+ tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
+ kwargs["tokenizer"] = tokenizer
+ kwargs["token_list"] = tokenizer.token_list
+ vocab_size = len(tokenizer.token_list)
+ else:
+ vocab_size = -1
+
+ # build frontend
+ frontend = kwargs.get("frontend", None)
+ if frontend is not None:
+ frontend_class = tables.frontend_classes.get(frontend)
+ frontend = frontend_class(**kwargs["frontend_conf"])
+ kwargs["frontend"] = frontend
+ kwargs["input_size"] = frontend.output_size()
+
+ # build model
+ model_class = tables.model_classes.get(kwargs["model"])
+ model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
+
+ model.to(device)
+
+ # init_param
+ init_param = kwargs.get("init_param", None)
+ if init_param is not None:
+ logging.info(f"Loading pretrained params from {init_param}")
+ load_pretrained_model(
+ model=model,
+ path=init_param,
+ ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
+ oss_bucket=kwargs.get("oss_bucket", None),
+ scope_map=kwargs.get("scope_map", None),
+ excludes=kwargs.get("excludes", None),
+ )
+
+ return model, kwargs
+
+ def __call__(self, *args, **cfg):
+ kwargs = self.kwargs
+ kwargs.update(cfg)
+ res = self.model(*args, kwargs)
+ return res
+
+ def generate(self, input, input_len=None, **cfg):
+ if self.vad_model is None:
+ return self.inference(input, input_len=input_len, **cfg)
+
+ else:
+ return self.inference_with_vad(input, input_len=input_len, **cfg)
+
+ def inference(
+ self, input, input_len=None, model=None, kwargs=None, key=None, **cfg
+ ):
+ kwargs = self.kwargs if kwargs is None else kwargs
+ kwargs.update(cfg)
+ model = self.model if model is None else model
+ model = model.cuda()
+ model.eval()
+
+ batch_size = kwargs.get("batch_size", 1)
+ # if kwargs.get("device", "cpu") == "cpu":
+ # batch_size = 1
+
+ key_list, data_list = prepare_data_iterator(
+ input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key
+ )
+
+ speed_stats = {}
+ asr_result_list = []
+ num_samples = len(data_list)
+ disable_pbar = kwargs.get("disable_pbar", False)
+ pbar = (
+ tqdm(colour="blue", total=num_samples, dynamic_ncols=True)
+ if not disable_pbar
+ else None
+ )
+ time_speech_total = 0.0
+ time_escape_total = 0.0
+ for beg_idx in range(0, num_samples, batch_size):
+ end_idx = min(num_samples, beg_idx + batch_size)
+ data_batch = data_list[beg_idx:end_idx]
+ key_batch = key_list[beg_idx:end_idx]
+ batch = {"data_in": data_batch, "key": key_batch}
+ if (end_idx - beg_idx) == 1 and kwargs.get(
+ "data_type", None
+ ) == "fbank": # fbank
+ batch["data_in"] = data_batch[0]
+ batch["data_lengths"] = input_len
+
+ time1 = time.perf_counter()
+ with torch.no_grad():
+ results, meta_data = model.inference(**batch, **kwargs)
+ time2 = time.perf_counter()
+
+ asr_result_list.extend(results)
+
+ # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
+ batch_data_time = meta_data.get("batch_data_time", -1)
+ time_escape = time2 - time1
+ speed_stats["load_data"] = meta_data.get("load_data", 0.0)
+ speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
+ speed_stats["forward"] = f"{time_escape:0.3f}"
+ speed_stats["batch_size"] = f"{len(results)}"
+ speed_stats["time_cost"] = f"{(time_escape)}"
+ speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
+ description = f"{speed_stats}, "
+ if pbar:
+ pbar.update(1)
+ pbar.set_description(description)
+ time_speech_total += batch_data_time
+ time_escape_total += time_escape
+
+ if pbar:
+ # pbar.update(1)
+ pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
+ torch.cuda.empty_cache()
+ return asr_result_list
+
+ def inference_with_vad(self, input, input_len=None, **cfg):
+
+ # step.1: compute the vad model
+ self.vad_kwargs.update(cfg)
+ beg_vad = time.time()
+ res = self.inference(
+ input,
+ input_len=input_len,
+ model=self.vad_model,
+ kwargs=self.vad_kwargs,
+ **cfg,
+ )
+ end_vad = time.time()
+ print(f"time cost vad: {end_vad - beg_vad:0.3f}")
+
+ # step.2 compute asr model
+ model = self.model
+ kwargs = self.kwargs
+ kwargs.update(cfg)
+ batch_size = int(kwargs.get("batch_size_s", 300)) * 1000
+ batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60)) * 1000
+ kwargs["batch_size"] = batch_size
+
+ key_list, data_list = prepare_data_iterator(
+ input, input_len=input_len, data_type=kwargs.get("data_type", None)
+ )
+ results_ret_list = []
+ time_speech_total_all_samples = 1e-6
+
+ beg_total = time.time()
+ pbar_total = tqdm(colour="red", total=len(res), dynamic_ncols=True)
+ for i in range(len(res)):
+ key = res[i]["key"]
+ vadsegments = res[i]["value"]
+ input_i = data_list[i]
+ speech = load_audio_text_image_video(
+ input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000)
+ )
+ speech_lengths = len(speech)
+ n = len(vadsegments)
+ data_with_index = [(vadsegments[i], i) for i in range(n)]
+ sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
+ results_sorted = []
+
+ if not len(sorted_data):
+ logging.info("decoding, utt: {}, empty speech".format(key))
+ continue
+
+ if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
+ batch_size = max(
+ batch_size, sorted_data[0][0][1] - sorted_data[0][0][0]
+ )
+
+ batch_size_ms_cum = 0
+ beg_idx = 0
+ beg_asr_total = time.time()
+ time_speech_total_per_sample = speech_lengths / 16000
+ time_speech_total_all_samples += time_speech_total_per_sample
+
+ all_segments = []
+ for j, _ in enumerate(range(0, n)):
+ # pbar_sample.update(1)
+ batch_size_ms_cum += sorted_data[j][0][1] - sorted_data[j][0][0]
+ if (
+ j < n - 1
+ and (
+ batch_size_ms_cum
+ + sorted_data[j + 1][0][1]
+ - sorted_data[j + 1][0][0]
+ )
+ < batch_size
+ and (sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0])
+ < batch_size_threshold_ms
+ ):
+ continue
+ batch_size_ms_cum = 0
+ end_idx = j + 1
+ speech_j, speech_lengths_j = slice_padding_audio_samples(
+ speech, speech_lengths, sorted_data[beg_idx:end_idx]
+ )
+ results = self.inference(
+ speech_j,
+ input_len=None,
+ model=model,
+ kwargs=kwargs,
+ disable_pbar=True,
+ **cfg,
+ )
+ if self.spk_model is not None:
+ # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
+ for _b in range(len(speech_j)):
+ vad_segments = [
+ [
+ sorted_data[beg_idx:end_idx][_b][0][0] / 1000.0,
+ sorted_data[beg_idx:end_idx][_b][0][1] / 1000.0,
+ np.array(speech_j[_b]),
+ ]
+ ]
+ segments = sv_chunk(vad_segments)
+ all_segments.extend(segments)
+ speech_b = [i[2] for i in segments]
+ spk_res = self.inference(
+ speech_b,
+ input_len=None,
+ model=self.spk_model,
+ kwargs=kwargs,
+ disable_pbar=True,
+ **cfg,
+ )
+ results[_b]["spk_embedding"] = spk_res[0]["spk_embedding"]
+ beg_idx = end_idx
+ if len(results) < 1:
+ continue
+ results_sorted.extend(results)
+
+ restored_data = [0] * n
+ for j in range(n):
+ index = sorted_data[j][1]
+ restored_data[index] = results_sorted[j]
+ result = {}
+
+ # results combine for texts, timestamps, speaker embeddings and others
+ # TODO: rewrite for clean code
+ for j in range(n):
+ for k, v in restored_data[j].items():
+ if k.startswith("timestamp"):
+ if k not in result:
+ result[k] = []
+ for t in restored_data[j][k]:
+ t[0] += vadsegments[j][0]
+ t[1] += vadsegments[j][0]
+ result[k].extend(restored_data[j][k])
+ elif k == "spk_embedding":
+ if k not in result:
+ result[k] = restored_data[j][k]
+ else:
+ result[k] = torch.cat(
+ [result[k], restored_data[j][k]], dim=0
+ )
+ elif "text" in k:
+ if k not in result:
+ result[k] = restored_data[j][k]
+ else:
+ result[k] += " " + restored_data[j][k]
+ else:
+ if k not in result:
+ result[k] = restored_data[j][k]
+ else:
+ result[k] += restored_data[j][k]
+
+ return_raw_text = kwargs.get("return_raw_text", False)
+ # step.3 compute punc model
+ if self.punc_model is not None:
+ self.punc_kwargs.update(cfg)
+ punc_res = self.inference(
+ result["text"],
+ model=self.punc_model,
+ kwargs=self.punc_kwargs,
+ disable_pbar=True,
+ **cfg,
+ )
+ raw_text = copy.copy(result["text"])
+ if return_raw_text:
+ result["raw_text"] = raw_text
+ result["text"] = punc_res[0]["text"]
+ else:
+ raw_text = None
+
+ # speaker embedding cluster after resorted
+ if self.spk_model is not None and kwargs.get("return_spk_res", True):
+ if raw_text is None:
+ logging.error("Missing punc_model, which is required by spk_model.")
+ all_segments = sorted(all_segments, key=lambda x: x[0])
+ spk_embedding = result["spk_embedding"]
+ labels = self.cb_model(
+ spk_embedding.cpu(), oracle_num=kwargs.get("preset_spk_num", None)
+ )
+ # del result['spk_embedding']
+ sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
+ if self.spk_mode == "vad_segment": # recover sentence_list
+ sentence_list = []
+ for res, vadsegment in zip(restored_data, vadsegments):
+ if "timestamp" not in res:
+ logging.error(
+ "Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \
+ and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
+ can predict timestamp, and speaker diarization relies on timestamps."
+ )
+ sentence_list.append(
+ {
+ "start": vadsegment[0],
+ "end": vadsegment[1],
+ "sentence": res["text"],
+ "timestamp": res["timestamp"],
+ }
+ )
+ elif self.spk_mode == "punc_segment":
+ if "timestamp" not in result:
+ logging.error(
+ "Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \
+ and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
+ can predict timestamp, and speaker diarization relies on timestamps."
+ )
+ sentence_list = timestamp_sentence(
+ punc_res[0]["punc_array"],
+ result["timestamp"],
+ raw_text,
+ return_raw_text=return_raw_text,
+ )
+ distribute_spk(sentence_list, sv_output)
+ result["sentence_info"] = sentence_list
+ elif kwargs.get("sentence_timestamp", False):
+ sentence_list = timestamp_sentence(
+ punc_res[0]["punc_array"],
+ result["timestamp"],
+ raw_text,
+ return_raw_text=return_raw_text,
+ )
+ result["sentence_info"] = sentence_list
+ if "spk_embedding" in result:
+ del result["spk_embedding"]
+
+ result["key"] = key
+ results_ret_list.append(result)
+ end_asr_total = time.time()
+ time_escape_total_per_sample = end_asr_total - beg_asr_total
+ pbar_total.update(1)
+ pbar_total.set_description(
+ f"rtf_avg: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
+ f"time_speech: {time_speech_total_per_sample: 0.3f}, "
+ f"time_escape: {time_escape_total_per_sample:0.3f}"
+ )
+
+ return results_ret_list
+
+ def infer_encoder(
+ self, input, input_len=None, model=None, kwargs=None, key=None, **cfg
+ ):
+ kwargs = self.kwargs if kwargs is None else kwargs
+ kwargs.update(cfg)
+ model = self.model if model is None else model
+ model = model.cuda()
+ model.eval()
+
+ batch_size = kwargs.get("batch_size", 1)
+
+ key_list, data_list = prepare_data_iterator(
+ input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key
+ )
+
+ asr_result_list = []
+ num_samples = len(data_list)
+ for beg_idx in range(0, num_samples, batch_size):
+ end_idx = min(num_samples, beg_idx + batch_size)
+ data_batch = data_list[beg_idx:end_idx]
+ key_batch = key_list[beg_idx:end_idx]
+ batch = {"data_in": data_batch, "key": key_batch}
+ if (end_idx - beg_idx) == 1 and kwargs.get(
+ "data_type", None
+ ) == "fbank": # fbank
+ batch["data_in"] = data_batch[0]
+ batch["data_lengths"] = input_len
+
+ with torch.no_grad():
+ results, meta_data, cache = model.infer_encoder(**batch, **kwargs)
+ asr_result_list.extend(results)
+
+ torch.cuda.empty_cache()
+ return asr_result_list, cache
diff --git a/almeval/models/stepaudio/funasr_detach/auto/auto_tokenizer.py b/almeval/models/stepaudio/funasr_detach/auto/auto_tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..59ff8893d9ccadd6971b9a9d982116ae82e09255
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/auto/auto_tokenizer.py
@@ -0,0 +1,7 @@
+class AutoTokenizer:
+ """
+ Undo
+ """
+
+ def __init__(self):
+ pass
diff --git a/almeval/models/stepaudio/funasr_detach/bin/__init__.py b/almeval/models/stepaudio/funasr_detach/bin/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/almeval/models/stepaudio/funasr_detach/bin/compute_audio_cmvn.py b/almeval/models/stepaudio/funasr_detach/bin/compute_audio_cmvn.py
new file mode 100644
index 0000000000000000000000000000000000000000..3459c71bfe8f63568f86fbf9b22a12bcd502d16a
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/bin/compute_audio_cmvn.py
@@ -0,0 +1,152 @@
+import os
+import json
+import numpy as np
+import torch
+import hydra
+import logging
+from omegaconf import DictConfig, OmegaConf
+
+from funasr_detach.register import tables
+from funasr_detach.download.download_from_hub import download_model
+from funasr_detach.train_utils.set_all_random_seed import set_all_random_seed
+
+
+@hydra.main(config_name=None, version_base=None)
+def main_hydra(kwargs: DictConfig):
+ if kwargs.get("debug", False):
+ import pdb
+
+ pdb.set_trace()
+
+ assert "model" in kwargs
+ if "model_conf" not in kwargs:
+ logging.info(
+ "download models from model hub: {}".format(kwargs.get("model_hub", "ms"))
+ )
+ kwargs = download_model(is_training=kwargs.get("is_training", True), **kwargs)
+
+ main(**kwargs)
+
+
+def main(**kwargs):
+ print(kwargs)
+ # set random seed
+ tables.print()
+ set_all_random_seed(kwargs.get("seed", 0))
+ torch.backends.cudnn.enabled = kwargs.get(
+ "cudnn_enabled", torch.backends.cudnn.enabled
+ )
+ torch.backends.cudnn.benchmark = kwargs.get(
+ "cudnn_benchmark", torch.backends.cudnn.benchmark
+ )
+ torch.backends.cudnn.deterministic = kwargs.get("cudnn_deterministic", True)
+
+ tokenizer = kwargs.get("tokenizer", None)
+
+ # build frontend if frontend is none None
+ frontend = kwargs.get("frontend", None)
+ if frontend is not None:
+ frontend_class = tables.frontend_classes.get(frontend)
+ frontend = frontend_class(**kwargs["frontend_conf"])
+ kwargs["frontend"] = frontend
+ kwargs["input_size"] = frontend.output_size()
+
+ # dataset
+ dataset_class = tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset"))
+ dataset_train = dataset_class(
+ kwargs.get("train_data_set_list"),
+ frontend=frontend,
+ tokenizer=None,
+ is_training=False,
+ **kwargs.get("dataset_conf")
+ )
+
+ # dataloader
+ batch_sampler = kwargs["dataset_conf"].get(
+ "batch_sampler", "DynamicBatchLocalShuffleSampler"
+ )
+ batch_sampler_train = None
+ if batch_sampler is not None:
+ batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler)
+ dataset_conf = kwargs.get("dataset_conf")
+ dataset_conf["batch_type"] = "example"
+ dataset_conf["batch_size"] = 1
+ batch_sampler_train = batch_sampler_class(
+ dataset_train, is_training=False, **dataset_conf
+ )
+
+ dataloader_train = torch.utils.data.DataLoader(
+ dataset_train,
+ collate_fn=dataset_train.collator,
+ batch_sampler=batch_sampler_train,
+ num_workers=int(kwargs.get("dataset_conf").get("num_workers", 4)),
+ pin_memory=True,
+ )
+
+ iter_stop = int(kwargs.get("scale", 1.0) * len(dataloader_train))
+
+ total_frames = 0
+ for batch_idx, batch in enumerate(dataloader_train):
+ if batch_idx >= iter_stop:
+ break
+
+ fbank = batch["speech"].numpy()[0, :, :]
+ if total_frames == 0:
+ mean_stats = np.sum(fbank, axis=0)
+ var_stats = np.sum(np.square(fbank), axis=0)
+ else:
+ mean_stats += np.sum(fbank, axis=0)
+ var_stats += np.sum(np.square(fbank), axis=0)
+ total_frames += fbank.shape[0]
+
+ cmvn_info = {
+ "mean_stats": list(mean_stats.tolist()),
+ "var_stats": list(var_stats.tolist()),
+ "total_frames": total_frames,
+ }
+ cmvn_file = kwargs.get("cmvn_file", "cmvn.json")
+ # import pdb;pdb.set_trace()
+ with open(cmvn_file, "w") as fout:
+ fout.write(json.dumps(cmvn_info))
+
+ mean = -1.0 * mean_stats / total_frames
+ var = 1.0 / np.sqrt(var_stats / total_frames - mean * mean)
+ dims = mean.shape[0]
+ am_mvn = os.path.dirname(cmvn_file) + "/am.mvn"
+ with open(am_mvn, "w") as fout:
+ fout.write(
+ ""
+ + "\n"
+ + " "
+ + str(dims)
+ + " "
+ + str(dims)
+ + "\n"
+ + "[ 0 ]"
+ + "\n"
+ + " "
+ + str(dims)
+ + " "
+ + str(dims)
+ + "\n"
+ )
+ mean_str = (
+ str(list(mean)).replace(",", "").replace("[", "[ ").replace("]", " ]")
+ )
+ fout.write(" 0 " + mean_str + "\n")
+ fout.write(" " + str(dims) + " " + str(dims) + "\n")
+ var_str = str(list(var)).replace(",", "").replace("[", "[ ").replace("]", " ]")
+ fout.write(" 0 " + var_str + "\n")
+ fout.write("" + "\n")
+
+
+"""
+python funasr/bin/compute_audio_cmvn.py \
+--config-path "/Users/zhifu/funasr1.0/examples/aishell/paraformer/conf" \
+--config-name "train_asr_paraformer_conformer_12e_6d_2048_256.yaml" \
+++train_data_set_list="/Users/zhifu/funasr1.0/data/list/audio_datasets.jsonl" \
+++cmvn_file="/Users/zhifu/funasr1.0/data/list/cmvn.json" \
+++dataset_conf.num_workers=0
+"""
+if __name__ == "__main__":
+ main_hydra()
diff --git a/almeval/models/stepaudio/funasr_detach/bin/inference.py b/almeval/models/stepaudio/funasr_detach/bin/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..f53ae31024380cdcf731af0d78a0d46a53d3d03d
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/bin/inference.py
@@ -0,0 +1,33 @@
+import hydra
+import logging
+from omegaconf import DictConfig, OmegaConf, ListConfig
+
+from funasr_detach.auto.auto_model import AutoModel
+
+
+@hydra.main(config_name=None, version_base=None)
+def main_hydra(cfg: DictConfig):
+ def to_plain_list(cfg_item):
+ if isinstance(cfg_item, ListConfig):
+ return OmegaConf.to_container(cfg_item, resolve=True)
+ elif isinstance(cfg_item, DictConfig):
+ return {k: to_plain_list(v) for k, v in cfg_item.items()}
+ else:
+ return cfg_item
+
+ kwargs = to_plain_list(cfg)
+ log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
+
+ logging.basicConfig(level=log_level)
+
+ if kwargs.get("debug", False):
+ import pdb
+
+ pdb.set_trace()
+ model = AutoModel(**kwargs)
+ res = model.generate(input=kwargs["input"])
+ print(res)
+
+
+if __name__ == "__main__":
+ main_hydra()
diff --git a/almeval/models/stepaudio/funasr_detach/bin/tokenize_text.py b/almeval/models/stepaudio/funasr_detach/bin/tokenize_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..cad9892a4fcbea40413d5effe78c74e01020bcae
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/bin/tokenize_text.py
@@ -0,0 +1,281 @@
+#!/usr/bin/env python3
+import argparse
+from collections import Counter
+import logging
+from pathlib import Path
+import sys
+from typing import List
+from typing import Optional
+
+
+from funasr_detach.utils.cli_utils import get_commandline_args
+from funasr_detach.tokenizer.build_tokenizer import build_tokenizer
+from funasr_detach.tokenizer.cleaner import TextCleaner
+from funasr_detach.tokenizer.phoneme_tokenizer import g2p_classes
+from funasr_detach.utils.types import str2bool
+from funasr_detach.utils.types import str_or_none
+
+
+def field2slice(field: Optional[str]) -> slice:
+ """Convert field string to slice
+
+ Note that field string accepts 1-based integer.
+
+ Examples:
+ >>> field2slice("1-")
+ slice(0, None, None)
+ >>> field2slice("1-3")
+ slice(0, 3, None)
+ >>> field2slice("-3")
+ slice(None, 3, None)
+ """
+ field = field.strip()
+ try:
+ if "-" in field:
+ # e.g. "2-" or "2-5" or "-7"
+ s1, s2 = field.split("-", maxsplit=1)
+ if s1.strip() == "":
+ s1 = None
+ else:
+ s1 = int(s1)
+ if s1 == 0:
+ raise ValueError("1-based string")
+ if s2.strip() == "":
+ s2 = None
+ else:
+ s2 = int(s2)
+ else:
+ # e.g. "2"
+ s1 = int(field)
+ s2 = s1 + 1
+ if s1 == 0:
+ raise ValueError("must be 1 or more value")
+ except ValueError:
+ raise RuntimeError(f"Format error: e.g. '2-', '2-5', or '-5': {field}")
+
+ if s1 is None:
+ slic = slice(None, s2)
+ else:
+ # -1 because of 1-based integer following "cut" command
+ # e.g "1-3" -> slice(0, 3)
+ slic = slice(s1 - 1, s2)
+ return slic
+
+
+def tokenize(
+ input: str,
+ output: str,
+ field: Optional[str],
+ delimiter: Optional[str],
+ token_type: str,
+ space_symbol: str,
+ non_linguistic_symbols: Optional[str],
+ bpemodel: Optional[str],
+ log_level: str,
+ write_vocabulary: bool,
+ vocabulary_size: int,
+ remove_non_linguistic_symbols: bool,
+ cutoff: int,
+ add_symbol: List[str],
+ cleaner: Optional[str],
+ g2p: Optional[str],
+):
+
+ logging.basicConfig(
+ level=log_level,
+ format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
+ )
+ if input == "-":
+ fin = sys.stdin
+ else:
+ fin = Path(input).open("r", encoding="utf-8")
+ if output == "-":
+ fout = sys.stdout
+ else:
+ p = Path(output)
+ p.parent.mkdir(parents=True, exist_ok=True)
+ fout = p.open("w", encoding="utf-8")
+
+ cleaner = TextCleaner(cleaner)
+ tokenizer = build_tokenizer(
+ token_type=token_type,
+ bpemodel=bpemodel,
+ delimiter=delimiter,
+ space_symbol=space_symbol,
+ non_linguistic_symbols=non_linguistic_symbols,
+ remove_non_linguistic_symbols=remove_non_linguistic_symbols,
+ g2p_type=g2p,
+ )
+
+ counter = Counter()
+ if field is not None:
+ field = field2slice(field)
+
+ for line in fin:
+ line = line.rstrip()
+ if field is not None:
+ # e.g. field="2-"
+ # uttidA hello world!! -> hello world!!
+ tokens = line.split(delimiter)
+ tokens = tokens[field]
+ if delimiter is None:
+ line = " ".join(tokens)
+ else:
+ line = delimiter.join(tokens)
+
+ line = cleaner(line)
+ tokens = tokenizer.text2tokens(line)
+ if not write_vocabulary:
+ fout.write(" ".join(tokens) + "\n")
+ else:
+ for t in tokens:
+ counter[t] += 1
+
+ if not write_vocabulary:
+ return
+
+ ## FIXME
+ ## del duplicate add_symbols in counter
+ for symbol_and_id in add_symbol:
+ # e.g symbol=":0"
+ try:
+ symbol, idx = symbol_and_id.split(":")
+ except ValueError:
+ raise RuntimeError(f"Format error: e.g. ':0': {symbol_and_id}")
+ symbol = symbol.strip()
+ if symbol in counter:
+ del counter[symbol]
+
+ # ======= write_vocabulary mode from here =======
+ # Sort by the number of occurrences in descending order
+ # and filter lower frequency words than cutoff value
+ words_and_counts = list(
+ filter(lambda x: x[1] > cutoff, sorted(counter.items(), key=lambda x: -x[1]))
+ )
+ # Restrict the vocabulary size
+ if vocabulary_size > 0:
+ if vocabulary_size < len(add_symbol):
+ raise RuntimeError(f"vocabulary_size is too small: {vocabulary_size}")
+ words_and_counts = words_and_counts[: vocabulary_size - len(add_symbol)]
+
+ # Parse the values of --add_symbol
+ for symbol_and_id in add_symbol:
+ # e.g symbol=":0"
+ try:
+ symbol, idx = symbol_and_id.split(":")
+ idx = int(idx)
+ except ValueError:
+ raise RuntimeError(f"Format error: e.g. ':0': {symbol_and_id}")
+ symbol = symbol.strip()
+
+ # e.g. idx=0 -> append as the first symbol
+ # e.g. idx=-1 -> append as the last symbol
+ if idx < 0:
+ idx = len(words_and_counts) + 1 + idx
+ words_and_counts.insert(idx, (symbol, None))
+
+ # Write words
+ for w, c in words_and_counts:
+ fout.write(w + "\n")
+
+ # Logging
+ total_count = sum(counter.values())
+ invocab_count = sum(c for w, c in words_and_counts if c is not None)
+ logging.info(f"OOV rate = {(total_count - invocab_count) / total_count * 100} %")
+
+
+def get_parser() -> argparse.ArgumentParser:
+ parser = argparse.ArgumentParser(
+ description="Tokenize texts",
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+ )
+ parser.add_argument(
+ "--log_level",
+ type=lambda x: x.upper(),
+ default="INFO",
+ choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
+ help="The verbose level of logging",
+ )
+
+ parser.add_argument(
+ "--input", "-i", required=True, help="Input text. - indicates sys.stdin"
+ )
+ parser.add_argument(
+ "--output", "-o", required=True, help="Output text. - indicates sys.stdout"
+ )
+ parser.add_argument(
+ "--field",
+ "-f",
+ help="The target columns of the input text as 1-based integer. e.g 2-",
+ )
+ parser.add_argument(
+ "--token_type",
+ "-t",
+ default="char",
+ choices=["char", "bpe", "word", "phn"],
+ help="Token type",
+ )
+ parser.add_argument("--delimiter", "-d", default=None, help="The delimiter")
+ parser.add_argument("--space_symbol", default="", help="The space symbol")
+ parser.add_argument("--bpemodel", default=None, help="The bpemodel file path")
+ parser.add_argument(
+ "--non_linguistic_symbols",
+ type=str_or_none,
+ help="non_linguistic_symbols file path",
+ )
+ parser.add_argument(
+ "--remove_non_linguistic_symbols",
+ type=str2bool,
+ default=False,
+ help="Remove non-language-symbols from tokens",
+ )
+ parser.add_argument(
+ "--cleaner",
+ type=str_or_none,
+ choices=[None, "tacotron", "jaconv", "vietnamese", "korean_cleaner"],
+ default=None,
+ help="Apply text cleaning",
+ )
+ parser.add_argument(
+ "--g2p",
+ type=str_or_none,
+ choices=g2p_classes,
+ default=None,
+ help="Specify g2p method if --token_type=phn",
+ )
+
+ group = parser.add_argument_group("write_vocabulary mode related")
+ group.add_argument(
+ "--write_vocabulary",
+ type=str2bool,
+ default=False,
+ help="Write tokens list instead of tokenized text per line",
+ )
+ group.add_argument("--vocabulary_size", type=int, default=0, help="Vocabulary size")
+ group.add_argument(
+ "--cutoff",
+ default=0,
+ type=int,
+ help="cut-off frequency used for write-vocabulary mode",
+ )
+ group.add_argument(
+ "--add_symbol",
+ type=str,
+ default=[],
+ action="append",
+ help="Append symbol e.g. --add_symbol ':0' --add_symbol ':1'",
+ )
+
+ return parser
+
+
+def main(cmd=None):
+ print(get_commandline_args(), file=sys.stderr)
+ parser = get_parser()
+ args = parser.parse_args(cmd)
+ kwargs = vars(args)
+ tokenize(**kwargs)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/almeval/models/stepaudio/funasr_detach/bin/train.py b/almeval/models/stepaudio/funasr_detach/bin/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..cceeb781433914b28af5341c3be932eb8c32cef6
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/bin/train.py
@@ -0,0 +1,227 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+
+import os
+import sys
+import torch
+import hydra
+import logging
+import argparse
+from io import BytesIO
+import torch.distributed as dist
+from collections.abc import Sequence
+from omegaconf import DictConfig, OmegaConf
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+
+from funasr_detach.register import tables
+from funasr_detach.optimizers import optim_classes
+from funasr_detach.train_utils.trainer import Trainer
+from funasr_detach.schedulers import scheduler_classes
+from funasr_detach.train_utils.initialize import initialize
+from funasr_detach.download.download_from_hub import download_model
+from funasr_detach.models.lora.utils import mark_only_lora_as_trainable
+from funasr_detach.train_utils.set_all_random_seed import set_all_random_seed
+from funasr_detach.train_utils.load_pretrained_model import load_pretrained_model
+
+# from funasr_detach.tokenizer.build_tokenizer import build_tokenizer
+# from funasr_detach.tokenizer.token_id_converter import TokenIDConverter
+# from funasr_detach.tokenizer.funtoken import build_tokenizer
+
+
+@hydra.main(config_name=None, version_base=None)
+def main_hydra(kwargs: DictConfig):
+ if kwargs.get("debug", False):
+ import pdb
+
+ pdb.set_trace()
+
+ assert "model" in kwargs
+ if "model_conf" not in kwargs:
+ logging.info(
+ "download models from model hub: {}".format(kwargs.get("model_hub", "ms"))
+ )
+ kwargs = download_model(is_training=kwargs.get("is_training", True), **kwargs)
+
+ main(**kwargs)
+
+
+def main(**kwargs):
+ print(kwargs)
+
+ # set random seed
+ set_all_random_seed(kwargs.get("seed", 0))
+ torch.backends.cudnn.enabled = kwargs.get(
+ "cudnn_enabled", torch.backends.cudnn.enabled
+ )
+ torch.backends.cudnn.benchmark = kwargs.get(
+ "cudnn_benchmark", torch.backends.cudnn.benchmark
+ )
+ torch.backends.cudnn.deterministic = kwargs.get("cudnn_deterministic", True)
+
+ local_rank = int(os.environ.get("LOCAL_RANK", 0))
+ if local_rank == 0:
+ tables.print()
+ # Check if we are using DDP or FSDP
+ use_ddp = "WORLD_SIZE" in os.environ and int(os.environ["WORLD_SIZE"]) > 1
+ use_fsdp = kwargs.get("use_fsdp", None)
+ if use_ddp or use_fsdp:
+ dist.init_process_group(
+ backend=kwargs.get("backend", "nccl"), init_method="env://"
+ )
+ torch.cuda.set_device(local_rank)
+
+ # save config.yaml
+ if (
+ (use_ddp or use_fsdp)
+ and dist.get_rank() == 0
+ or not (use_ddp or use_fsdp)
+ and local_rank == 0
+ ):
+ os.makedirs(kwargs.get("output_dir", "./"), exist_ok=True)
+ yaml_file = os.path.join(kwargs.get("output_dir", "./"), "config.yaml")
+ OmegaConf.save(config=kwargs, f=yaml_file)
+ logging.info("config.yaml is saved to: %s", yaml_file)
+
+ tokenizer = kwargs.get("tokenizer", None)
+ if tokenizer is not None:
+ tokenizer_class = tables.tokenizer_classes.get(tokenizer)
+ tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
+ kwargs["tokenizer"] = tokenizer
+
+ # build frontend if frontend is none None
+ frontend = kwargs.get("frontend", None)
+ if frontend is not None:
+ frontend_class = tables.frontend_classes.get(frontend)
+ frontend = frontend_class(**kwargs["frontend_conf"])
+ kwargs["frontend"] = frontend
+ kwargs["input_size"] = frontend.output_size()
+
+ # build model
+ model_class = tables.model_classes.get(kwargs["model"])
+ model = model_class(
+ **kwargs, **kwargs["model_conf"], vocab_size=len(tokenizer.token_list)
+ )
+
+ # init_param
+ init_param = kwargs.get("init_param", None)
+ if init_param is not None:
+ if not isinstance(init_param, (list, tuple)):
+ init_param = (init_param,)
+ logging.info("init_param is not None: %s", init_param)
+ for p in init_param:
+ logging.info(f"Loading pretrained params from {p}")
+ load_pretrained_model(
+ model=model,
+ path=p,
+ ignore_init_mismatch=kwargs.get("ignore_init_mismatch", True),
+ oss_bucket=kwargs.get("oss_bucket", None),
+ scope_map=kwargs.get("scope_map", None),
+ excludes=kwargs.get("excludes", None),
+ )
+ else:
+ initialize(model, kwargs.get("init", "kaiming_normal"))
+
+ # freeze_param
+ freeze_param = kwargs.get("freeze_param", None)
+ if freeze_param is not None:
+ freeze_param = eval(freeze_param)
+ if isinstance(freeze_param, Sequence):
+ freeze_param = (freeze_param,)
+ logging.info("freeze_param is not None: %s", freeze_param)
+ for t in freeze_param:
+ for k, p in model.named_parameters():
+ if k.startswith(t + ".") or k == t:
+ logging.info(f"Setting {k}.requires_grad = False")
+ p.requires_grad = False
+
+ if use_ddp:
+ model = model.cuda(local_rank)
+ model = DDP(
+ model,
+ device_ids=[local_rank],
+ find_unused_parameters=kwargs.get("train_conf", {}).get(
+ "find_unused_parameters", False
+ ),
+ )
+ elif use_fsdp:
+ model = FSDP(model).cuda(local_rank)
+ else:
+ model = model.to(device=kwargs.get("device", "cuda"))
+
+ # optim
+ optim = kwargs.get("optim", "adam")
+ assert optim in optim_classes
+ optim_class = optim_classes.get(optim)
+ optim = optim_class(model.parameters(), **kwargs.get("optim_conf"))
+
+ # scheduler
+ scheduler = kwargs.get("scheduler", "warmuplr")
+ assert scheduler in scheduler_classes
+ scheduler_class = scheduler_classes.get(scheduler)
+ scheduler = scheduler_class(optim, **kwargs.get("scheduler_conf"))
+
+ # dataset
+ dataset_class = tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset"))
+ dataset_tr = dataset_class(
+ kwargs.get("train_data_set_list"),
+ frontend=frontend,
+ tokenizer=tokenizer,
+ is_training=True,
+ **kwargs.get("dataset_conf"),
+ )
+ dataset_val = dataset_class(
+ kwargs.get("valid_data_set_list"),
+ frontend=frontend,
+ tokenizer=tokenizer,
+ is_training=False,
+ **kwargs.get("dataset_conf"),
+ )
+
+ # dataloader
+ batch_sampler = kwargs["dataset_conf"].get(
+ "batch_sampler", "DynamicBatchLocalShuffleSampler"
+ )
+ batch_sampler_val = None
+ if batch_sampler is not None:
+ batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler)
+ batch_sampler = batch_sampler_class(dataset_tr, **kwargs.get("dataset_conf"))
+ batch_sampler_val = batch_sampler_class(
+ dataset_val, is_training=False, **kwargs.get("dataset_conf")
+ )
+ dataloader_tr = torch.utils.data.DataLoader(
+ dataset_tr,
+ collate_fn=dataset_tr.collator,
+ batch_sampler=batch_sampler,
+ num_workers=kwargs.get("dataset_conf").get("num_workers", 4),
+ pin_memory=True,
+ )
+
+ dataloader_val = torch.utils.data.DataLoader(
+ dataset_val,
+ collate_fn=dataset_val.collator,
+ batch_sampler=batch_sampler_val,
+ num_workers=kwargs.get("dataset_conf").get("num_workers", 4),
+ pin_memory=True,
+ )
+ trainer = Trainer(
+ model=model,
+ optim=optim,
+ scheduler=scheduler,
+ dataloader_train=dataloader_tr,
+ dataloader_val=dataloader_val,
+ local_rank=local_rank,
+ use_ddp=use_ddp,
+ use_fsdp=use_fsdp,
+ output_dir=kwargs.get("output_dir", "./exp"),
+ resume=kwargs.get("resume", True),
+ **kwargs.get("train_conf"),
+ )
+ trainer.run()
+
+ if use_ddp or use_fsdp:
+ torch.distributed.destroy_process_group()
+
+
+if __name__ == "__main__":
+ main_hydra()
diff --git a/almeval/models/stepaudio/funasr_detach/datasets/__init__.py b/almeval/models/stepaudio/funasr_detach/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/__init__.py b/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/datasets.py b/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3f5c6522c4a81d52d32bbf1e51ebc6f52b7ca09
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/datasets.py
@@ -0,0 +1,112 @@
+import torch
+
+from funasr_detach.register import tables
+from funasr_detach.utils.load_utils import extract_fbank, load_audio_text_image_video
+
+
+@tables.register("dataset_classes", "AudioDataset")
+class AudioDataset(torch.utils.data.Dataset):
+ """
+ AudioDataset
+ """
+
+ def __init__(
+ self,
+ path,
+ index_ds: str = None,
+ frontend=None,
+ tokenizer=None,
+ int_pad_value: int = -1,
+ float_pad_value: float = 0.0,
+ **kwargs
+ ):
+ super().__init__()
+ index_ds_class = tables.index_ds_classes.get(index_ds)
+ self.index_ds = index_ds_class(path, **kwargs)
+ preprocessor_speech = kwargs.get("preprocessor_speech", None)
+ if preprocessor_speech:
+ preprocessor_speech_class = tables.preprocessor_classes.get(
+ preprocessor_speech
+ )
+ preprocessor_speech = preprocessor_speech_class(
+ **kwargs.get("preprocessor_speech_conf")
+ )
+ self.preprocessor_speech = preprocessor_speech
+ preprocessor_text = kwargs.get("preprocessor_text", None)
+ if preprocessor_text:
+ preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
+ preprocessor_text = preprocessor_text_class(
+ **kwargs.get("preprocessor_text_conf")
+ )
+ self.preprocessor_text = preprocessor_text
+
+ self.frontend = frontend
+ self.fs = 16000 if frontend is None else frontend.fs
+ self.data_type = "sound"
+ self.tokenizer = tokenizer
+
+ self.int_pad_value = int_pad_value
+ self.float_pad_value = float_pad_value
+
+ def get_source_len(self, index):
+ item = self.index_ds[index]
+ return self.index_ds.get_source_len(item)
+
+ def get_target_len(self, index):
+ item = self.index_ds[index]
+ return self.index_ds.get_target_len(item)
+
+ def __len__(self):
+ return len(self.index_ds)
+
+ def __getitem__(self, index):
+ item = self.index_ds[index]
+ # import pdb;
+ # pdb.set_trace()
+ source = item["source"]
+ data_src = load_audio_text_image_video(source, fs=self.fs)
+ if self.preprocessor_speech:
+ data_src = self.preprocessor_speech(data_src, fs=self.fs)
+ speech, speech_lengths = extract_fbank(
+ data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
+ ) # speech: [b, T, d]
+
+ target = item["target"]
+ if self.preprocessor_text:
+ target = self.preprocessor_text(target)
+ if self.tokenizer:
+ ids = self.tokenizer.encode(target)
+ text = torch.tensor(ids, dtype=torch.int64)
+ else:
+ ids = target
+ text = ids
+ ids_lengths = len(ids)
+ text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
+
+ return {
+ "speech": speech[0, :, :],
+ "speech_lengths": speech_lengths,
+ "text": text,
+ "text_lengths": text_lengths,
+ }
+
+ def collator(self, samples: list = None):
+ outputs = {}
+ for sample in samples:
+ for key in sample.keys():
+ if key not in outputs:
+ outputs[key] = []
+ outputs[key].append(sample[key])
+
+ for key, data_list in outputs.items():
+ if isinstance(data_list[0], torch.Tensor):
+ if data_list[0].dtype == torch.int64:
+
+ pad_value = self.int_pad_value
+ else:
+ pad_value = self.float_pad_value
+
+ outputs[key] = torch.nn.utils.rnn.pad_sequence(
+ data_list, batch_first=True, padding_value=pad_value
+ )
+ return outputs
diff --git a/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/index_ds.py b/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/index_ds.py
new file mode 100644
index 0000000000000000000000000000000000000000..90b606c887f4960357f3b7ea68f7eca9f1055f96
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/index_ds.py
@@ -0,0 +1,150 @@
+import os
+import json
+import torch
+import logging
+import concurrent.futures
+import librosa
+import torch.distributed as dist
+
+from funasr_detach.register import tables
+
+
+@tables.register("index_ds_classes", "IndexDSJsonlRankSplit")
+class IndexDSJsonlRankSplit(torch.utils.data.Dataset):
+
+ def __init__(self, path):
+ super().__init__()
+
+ contents = []
+ with open(path, encoding="utf-8") as fin:
+ for line in fin:
+ data = json.loads(line.strip())
+ if "text" in data: # for sft
+ self.contents.append(data["text"])
+ if "source" in data: # for speech lab pretrain
+ prompt = data["prompt"]
+ source = data["source"]
+ target = data["target"]
+ source_len = data["source_len"]
+ target_len = data["target_len"]
+
+ contents.append(
+ {
+ "source": source,
+ "prompt": prompt,
+ "target": target,
+ "source_len": source_len,
+ "target_len": target_len,
+ }
+ )
+
+ self.contents = []
+ total_num = len(contents)
+ try:
+ rank = dist.get_rank()
+ world_size = dist.get_world_size()
+ except:
+ rank = 0
+ world_size = 1
+ logging.warning("distributed is not initialized, only single shard")
+ num_per_rank = total_num // world_size
+
+ # rank = 0
+ # import ipdb; ipdb.set_trace()
+ self.contents = contents[rank * num_per_rank : (rank + 1) * num_per_rank]
+
+ logging.info(
+ "in rank: {}, num of samplers: {}, total_num of samplers across ranks: {}".format(
+ rank, len(self.contents), len(contents)
+ )
+ )
+
+ def __len__(self):
+ return len(self.contents)
+
+ def __getitem__(self, index):
+ try:
+ data = self.contents[index]
+ except:
+ print(index)
+ return data
+
+ def get_source_len(self, data_dict):
+ return data_dict["source_len"]
+
+ def get_target_len(self, data_dict):
+
+ return data_dict["target_len"] if "target_len" in data_dict else 0
+
+
+@tables.register("index_ds_classes", "IndexDSJsonl")
+@tables.register("index_ds_classes", "IndexDSJsonlRankFull")
+class IndexDSJsonlRankFull(torch.utils.data.Dataset):
+
+ def __init__(self, path: str, **kwargs):
+ super().__init__()
+
+ if isinstance(path, (list, tuple)): # wav.scp, text.txt/text.trans
+ from funasr_detach.datasets.audio_datasets.scp2jsonl import (
+ gen_jsonl_from_wav_text_list,
+ )
+
+ jsonl_outdir = os.path.dirname(path[0])
+ jsonl_name = (
+ "datalist_train.jsonl"
+ if kwargs.get("is_training", True)
+ else "datalist_val.jsonl"
+ )
+ jsonl_file_out = os.path.join(jsonl_outdir, jsonl_name)
+ if not os.path.exists(jsonl_file_out):
+ print(f"datalist is: {path}, generate jsonl from it")
+ gen_jsonl_from_wav_text_list(
+ path, jsonl_file_out=jsonl_file_out, **kwargs
+ )
+ path = jsonl_file_out
+
+ contents = []
+ with open(path, encoding="utf-8") as fin:
+ for line in fin:
+ data = json.loads(line.strip())
+ if "text" in data: # for sft
+ self.contents.append(data["text"])
+ if "source" in data: # for speech lab pretrain
+ prompt = data.get("prompt", "")
+ source = data["source"]
+ target = data["target"]
+ source_len = data.get("source_len", 1)
+ target_len = data.get("target_len", 0)
+
+ contents.append(
+ {
+ "source": source,
+ "prompt": prompt,
+ "target": target,
+ "source_len": source_len,
+ "target_len": target_len,
+ }
+ )
+
+ self.contents = contents
+
+ logging.info(
+ "total_num of samplers across ranks: {}".format(len(self.contents))
+ )
+
+ def __len__(self):
+ return len(self.contents)
+
+ def __getitem__(self, index):
+ try:
+ data = self.contents[index]
+ except:
+ print(index)
+ return data
+
+ def get_source_len(self, data_dict):
+ return data_dict.get("source_len", 1)
+
+ def get_target_len(self, data_dict):
+
+ return data_dict.get("target_len", 0)
diff --git a/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/preprocessor.py b/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/preprocessor.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1c4c8322eb6405bb6d1489dd01cd89df9ca9ef6
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/preprocessor.py
@@ -0,0 +1,55 @@
+import os
+import json
+import torch
+import logging
+import concurrent.futures
+import librosa
+import torch.distributed as dist
+from typing import Collection
+import torch
+import torchaudio
+from torch import nn
+import random
+import re
+from funasr_detach.tokenizer.cleaner import TextCleaner
+from funasr_detach.register import tables
+
+
+@tables.register("preprocessor_classes", "SpeechPreprocessSpeedPerturb")
+class SpeechPreprocessSpeedPerturb(nn.Module):
+ def __init__(self, speed_perturb: list = None, **kwargs):
+ super().__init__()
+ self.speed_perturb = speed_perturb
+
+ def forward(self, waveform, fs, **kwargs):
+ if self.speed_perturb is None:
+ return waveform
+ speed = random.choice(self.speed_perturb)
+ if speed != 1.0:
+ if not isinstance(waveform, torch.Tensor):
+ waveform = torch.tensor(waveform)
+ waveform, _ = torchaudio.sox_effects.apply_effects_tensor(
+ waveform.view(1, -1), fs, [["speed", str(speed)], ["rate", str(fs)]]
+ )
+ waveform = waveform.view(-1)
+
+ return waveform
+
+
+@tables.register("preprocessor_classes", "TextPreprocessSegDict")
+class TextPreprocessSegDict(nn.Module):
+ def __init__(
+ self,
+ seg_dict: str = None,
+ text_cleaner: Collection[str] = None,
+ split_with_space: bool = False,
+ **kwargs
+ ):
+ super().__init__()
+
+ self.text_cleaner = TextCleaner(text_cleaner)
+
+ def forward(self, text, **kwargs):
+ text = self.text_cleaner(text)
+
+ return text
diff --git a/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/samplers.py b/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/samplers.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4b80936b170325d190678180ff9f1b32d9dd3d5
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/samplers.py
@@ -0,0 +1,306 @@
+import torch
+import numpy as np
+import logging
+import torch.distributed as dist
+
+from funasr_detach.register import tables
+
+
+@tables.register("batch_sampler_classes", "DynamicBatchLocalShuffleSampler")
+class BatchSampler(torch.utils.data.BatchSampler):
+
+ def __init__(
+ self,
+ dataset,
+ batch_type: str = "example",
+ batch_size: int = 100,
+ buffer_size: int = 30,
+ drop_last: bool = False,
+ shuffle: bool = True,
+ is_training: bool = True,
+ **kwargs
+ ):
+
+ self.drop_last = drop_last
+ self.pre_idx = -1
+ self.dataset = dataset
+ self.total_samples = len(dataset)
+ self.batch_type = batch_type
+ self.batch_size = int(batch_size)
+ self.buffer_size = buffer_size
+ self.max_token_length = kwargs.get("max_token_length", 5000)
+ self.shuffle_idx = np.arange(self.total_samples)
+ self.shuffle = shuffle and is_training
+ self.length_scale_source = kwargs.get("length_scale_source", 1.0)
+
+ def __len__(self):
+ return (self.total_samples - 1) // self.batch_size + 1
+
+ def set_epoch(self, epoch):
+ np.random.seed(epoch)
+
+ def __iter__(self):
+
+ if self.shuffle:
+ np.random.shuffle(self.shuffle_idx)
+
+ batch = []
+ max_token = 0
+ num_sample = 0
+
+ iter_num = (self.total_samples - 1) // self.buffer_size + 1
+ # print("iter_num: ", iter_num)
+ for iter in range(self.pre_idx + 1, iter_num):
+ datalen_with_index = []
+ for i in range(self.buffer_size):
+ idx = iter * self.buffer_size + i
+ if idx >= self.total_samples:
+ continue
+
+ idx_map = self.shuffle_idx[idx]
+ # prompt = self.dataset.indexed_dataset[idx_map]["prompt"]
+ target_len = (
+ self.dataset.get_target_len(idx_map)
+ if self.batch_type == "length"
+ else 0.0
+ )
+ source_len = (
+ self.dataset.get_source_len(idx_map) / self.length_scale_source
+ )
+ sample_len_cur = source_len + target_len
+
+ datalen_with_index.append([idx, sample_len_cur])
+
+ datalen_with_index_sort = sorted(datalen_with_index, key=lambda x: x[1])
+ for item in datalen_with_index_sort:
+ idx, sample_len_cur_raw = item
+ if sample_len_cur_raw > self.max_token_length:
+ continue
+
+ max_token_cur = max(max_token, sample_len_cur_raw)
+ max_token_padding = 1 + num_sample
+ if self.batch_type != "example":
+ max_token_padding *= max_token_cur
+ if max_token_padding <= self.batch_size:
+ batch.append(idx)
+ max_token = max_token_cur
+ num_sample += 1
+ else:
+ yield batch
+ batch = [idx]
+ max_token = sample_len_cur_raw
+ num_sample = 1
+
+
+@tables.register("batch_sampler_classes", "BatchSampler")
+@tables.register("batch_sampler_classes", "RankFullLocalShuffleBatchSampler")
+class RankFullLocalShuffleBatchSampler(torch.utils.data.BatchSampler):
+
+ def __init__(
+ self,
+ dataset,
+ batch_type: str = "example",
+ batch_size: int = 100,
+ buffer_size: int = 30,
+ drop_last: bool = True,
+ shuffle: bool = True,
+ is_training: bool = True,
+ **kwargs
+ ):
+
+ self.drop_last = drop_last
+ self.pre_idx = -1
+ self.dataset = dataset
+ self.total_samples = len(dataset)
+ self.batch_type = batch_type
+ self.batch_size = int(batch_size)
+ self.buffer_size = buffer_size
+ self.max_token_length = kwargs.get("max_token_length", 1500)
+ self.shuffle_idx = np.arange(self.total_samples)
+ self.shuffle = shuffle and is_training
+ self.length_scale_source = kwargs.get("length_scale_source", 1.0)
+
+ try:
+ rank = dist.get_rank()
+ world_size = dist.get_world_size()
+ except:
+ rank = 0
+ world_size = 1
+ self.rank = rank
+ self.world_size = world_size
+
+ def __len__(self):
+ return (self.total_samples - 1) // (self.batch_size * self.world_size) + 1
+
+ def set_epoch(self, epoch):
+ np.random.seed(epoch)
+
+ def __iter__(self):
+
+ batch_size_total = self.batch_size * self.world_size
+
+ if self.shuffle:
+ np.random.shuffle(self.shuffle_idx)
+
+ batch = []
+ max_token = 0
+ num_sample = 0
+
+ iter_num = (self.total_samples - 1) // self.buffer_size + 1
+ # print("iter_num: ", iter_num)
+ for iter in range(self.pre_idx + 1, iter_num):
+ # if iter == iter_num -1 and self.drop_last:
+ # continue
+ datalen_with_index = []
+ for i in range(self.buffer_size):
+ idx = iter * self.buffer_size + i
+ if idx >= self.total_samples:
+ continue
+
+ idx_map = self.shuffle_idx[idx]
+ # prompt = self.dataset.indexed_dataset[idx_map]["prompt"]
+
+ source_len = (
+ self.dataset.get_source_len(idx_map) / self.length_scale_source
+ )
+ target_len = (
+ self.dataset.get_target_len(idx_map)
+ if self.batch_type == "length"
+ else 0.0
+ )
+ sample_len_cur = source_len + target_len
+
+ datalen_with_index.append([idx, sample_len_cur])
+
+ datalen_with_index_sort = sorted(datalen_with_index, key=lambda x: x[1])
+ for item in datalen_with_index_sort:
+ idx, sample_len_cur_raw = item
+ if sample_len_cur_raw > self.max_token_length:
+ continue
+
+ max_token_cur = max(max_token, sample_len_cur_raw)
+ max_token_padding = 1 + num_sample
+ # if self.batch_type != 'example':
+ # max_token_padding *= max_token_cur
+ if max_token_padding <= batch_size_total:
+ batch.append(idx)
+ max_token = max_token_cur
+ num_sample += 1
+ else:
+ batch_rank = batch[
+ self.rank * self.batch_size : (self.rank + 1) * self.batch_size
+ ]
+ yield batch_rank
+ batch = [idx]
+ max_token = sample_len_cur_raw
+ num_sample = 1
+
+
+@tables.register("batch_sampler_classes", "RankFullLocalShuffleDynamicBatchSampler")
+class RankFullLocalShuffleDynamicBatchSampler(torch.utils.data.BatchSampler):
+
+ def __init__(
+ self,
+ dataset,
+ batch_type: str = "example",
+ batch_size: int = 100,
+ buffer_size: int = 30,
+ drop_last: bool = True,
+ shuffle: bool = True,
+ is_training: bool = True,
+ **kwargs
+ ):
+
+ self.drop_last = drop_last
+ self.pre_idx = -1
+ self.dataset = dataset
+ self.total_samples = len(dataset)
+ self.batch_type = batch_type
+ self.batch_size = int(batch_size)
+ self.buffer_size = buffer_size
+ self.max_token_length = kwargs.get("max_token_length", 1500)
+ self.shuffle_idx = np.arange(self.total_samples)
+ self.shuffle = shuffle and is_training
+ self.length_scale_source = kwargs.get("length_scale_source", 1.0)
+
+ try:
+ rank = dist.get_rank()
+ world_size = dist.get_world_size()
+ except:
+ rank = 0
+ world_size = 1
+ self.rank = rank
+ self.world_size = world_size
+
+ def __len__(self):
+ return (self.total_samples - 1) // (self.batch_size * self.world_size) + 1
+
+ def set_epoch(self, epoch):
+ np.random.seed(epoch)
+
+ def __iter__(self):
+
+ batch_size_total = self.batch_size * self.world_size
+ if self.shuffle:
+ np.random.shuffle(self.shuffle_idx)
+
+ batch_list_all_rank = []
+ batch_list_cur = []
+ max_token = 0
+ num_sample = 0
+
+ iter_num = (self.total_samples - 1) // self.buffer_size + 1
+ # print("iter_num: ", iter_num)
+ for iter in range(self.pre_idx + 1, iter_num):
+ # if iter == iter_num - 1 and self.drop_last:
+ # continue
+ datalen_with_index = []
+ for i in range(self.buffer_size):
+ idx = iter * self.buffer_size + i
+ if idx >= self.total_samples:
+ continue
+
+ idx_map = self.shuffle_idx[idx]
+ # prompt = self.dataset.indexed_dataset[idx_map]["prompt"]
+
+ source_len = (
+ self.dataset.get_source_len(idx_map) / self.length_scale_source
+ )
+ target_len = (
+ self.dataset.get_target_len(idx_map)
+ if self.batch_type == "length"
+ else 0.0
+ )
+ sample_len_cur = source_len + target_len
+
+ datalen_with_index.append([idx, sample_len_cur])
+
+ datalen_with_index_sort = sorted(datalen_with_index, key=lambda x: x[1])
+ for ii, item in enumerate(datalen_with_index_sort):
+ is_last_batch = iter == iter_num - 1 and ii == len(
+ datalen_with_index_sort
+ )
+ idx, sample_len_cur_raw = item
+ if sample_len_cur_raw > self.max_token_length:
+ continue
+
+ max_token_cur = max(max_token, sample_len_cur_raw)
+ max_token_padding = 1 + num_sample
+
+ if self.batch_type != "example":
+ max_token_padding *= max_token_cur
+ if len(batch_list_all_rank) < self.world_size:
+
+ if max_token_padding <= self.batch_size:
+ batch_list_cur.append(idx)
+ max_token = max_token_cur
+ num_sample += 1
+ else:
+ batch_list_all_rank.append(batch_list_cur)
+ batch_list_cur = []
+ else:
+ batch_rank = batch_list_all_rank[self.rank]
+ yield batch_rank
+ batch_list_all_rank = [idx]
+ max_token = sample_len_cur_raw
+ num_sample = 1
diff --git a/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/scp2jsonl.py b/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/scp2jsonl.py
new file mode 100644
index 0000000000000000000000000000000000000000..275593749020ac79ee8c297a7c0118d9ab5f7c83
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/datasets/audio_datasets/scp2jsonl.py
@@ -0,0 +1,116 @@
+import os
+import json
+import torch
+import logging
+import hydra
+from omegaconf import DictConfig, OmegaConf
+import concurrent.futures
+import librosa
+import torch.distributed as dist
+
+
+def gen_jsonl_from_wav_text_list(
+ path, data_type_list=("source", "target"), jsonl_file_out: str = None, **kwargs
+):
+ try:
+ rank = dist.get_rank()
+ world_size = dist.get_world_size()
+ except:
+ rank = 0
+ world_size = 1
+
+ cpu_cores = os.cpu_count() or 1
+ print(f"convert wav.scp text to jsonl, ncpu: {cpu_cores}")
+ if rank == 0:
+ json_dict = {}
+ for data_type, data_file in zip(data_type_list, path):
+ json_dict[data_type] = {}
+ with open(data_file, "r") as f:
+
+ data_file_lists = f.readlines()
+ lines_for_each_th = (len(data_file_lists) - 1) // cpu_cores + 1
+ task_num = cpu_cores if len(data_file_lists) > cpu_cores else 1
+ with concurrent.futures.ThreadPoolExecutor(
+ max_workers=cpu_cores
+ ) as executor:
+
+ futures = [
+ executor.submit(
+ parse_context_length,
+ data_file_lists[
+ i * lines_for_each_th : (i + 1) * lines_for_each_th
+ ],
+ data_type,
+ )
+ for i in range(task_num)
+ ]
+
+ for future in concurrent.futures.as_completed(futures):
+
+ json_dict[data_type].update(future.result())
+ # print(json_dict)
+
+ with open(jsonl_file_out, "w") as f:
+ for key in json_dict[data_type_list[0]].keys():
+ jsonl_line = {"key": key}
+ for data_file in data_type_list:
+ jsonl_line.update(json_dict[data_file][key])
+ jsonl_line = json.dumps(jsonl_line, ensure_ascii=False)
+ f.write(jsonl_line + "\n")
+ f.flush()
+
+ else:
+ pass
+
+ if world_size > 1:
+ dist.barrier()
+
+
+def parse_context_length(data_list: list, data_type: str):
+
+ res = {}
+ for i, line in enumerate(data_list):
+ key, line = line.strip().split(maxsplit=1)
+ line = line.strip()
+ if os.path.exists(line):
+ waveform, _ = librosa.load(line, sr=16000)
+ sample_num = len(waveform)
+ context_len = int(sample_num // 16000 * 1000 / 10)
+ else:
+ context_len = len(line.split()) if " " in line else len(line)
+ res[key] = {data_type: line, f"{data_type}_len": context_len}
+ return res
+
+
+@hydra.main(config_name=None, version_base=None)
+def main_hydra(cfg: DictConfig):
+
+ kwargs = OmegaConf.to_container(cfg, resolve=True)
+
+ scp_file_list = kwargs.get(
+ "scp_file_list",
+ (
+ "/Users/zhifu/funasr1.0/test_local/wav.scp",
+ "/Users/zhifu/funasr1.0/test_local/text.txt",
+ ),
+ )
+ if isinstance(scp_file_list, str):
+ scp_file_list = eval(scp_file_list)
+ data_type_list = kwargs.get("data_type_list", ("source", "target"))
+ jsonl_file_out = kwargs.get(
+ "jsonl_file_out", "/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl"
+ )
+ gen_jsonl_from_wav_text_list(
+ scp_file_list, data_type_list=data_type_list, jsonl_file_out=jsonl_file_out
+ )
+
+
+"""
+python -m funasr_detach.datasets.audio_datasets.scp2jsonl \
+++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
+++data_type_list='["source", "target"]' \
+++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl
+"""
+
+if __name__ == "__main__":
+ main_hydra()
diff --git a/almeval/models/stepaudio/funasr_detach/download/__init__.py b/almeval/models/stepaudio/funasr_detach/download/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/almeval/models/stepaudio/funasr_detach/download/download_dataset_from_hub.py b/almeval/models/stepaudio/funasr_detach/download/download_dataset_from_hub.py
new file mode 100644
index 0000000000000000000000000000000000000000..6834137bc849c6b6f710d5b23168ebceb9a898ed
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/download/download_dataset_from_hub.py
@@ -0,0 +1,19 @@
+def download_dataset():
+ pass
+
+
+def download_dataset_from_ms(**kwargs):
+ from modelscope.msdatasets import MsDataset
+
+ dataset_name = kwargs.get(
+ "dataset_name", "speech_asr/speech_asr_aishell1_trainsets"
+ )
+ subset_name = kwargs.get("subset_name", "default")
+ split = kwargs.get("split", "train")
+ data_dump_dir = kwargs.get("data_dump_dir", None)
+ ds = MsDataset.load(
+ dataset_name=dataset_name,
+ subset_name=subset_name,
+ split=split,
+ cache_dir=data_dump_dir,
+ )
diff --git a/almeval/models/stepaudio/funasr_detach/download/download_from_hub.py b/almeval/models/stepaudio/funasr_detach/download/download_from_hub.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cfca6f75becbe5625cd444e4be0e08bcac4e31e
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/download/download_from_hub.py
@@ -0,0 +1,122 @@
+import os
+import json
+from omegaconf import OmegaConf
+
+from funasr_detach.download.name_maps_from_hub import name_maps_ms, name_maps_hf
+
+
+def download_model(**kwargs):
+ model_hub = kwargs.get("model_hub", "ms")
+ if model_hub == "ms":
+ kwargs = download_from_ms(**kwargs)
+
+ return kwargs
+
+
+def download_from_ms(**kwargs):
+ model_or_path = kwargs.get("model")
+ if model_or_path in name_maps_ms:
+ model_or_path = name_maps_ms[model_or_path]
+ model_revision = kwargs.get("model_revision")
+ if not os.path.exists(model_or_path):
+ model_or_path = get_or_download_model_dir(
+ model_or_path,
+ model_revision,
+ is_training=kwargs.get("is_training"),
+ check_latest=kwargs.get("kwargs", True),
+ )
+ kwargs["model_path"] = model_or_path
+
+ if os.path.exists(os.path.join(model_or_path, "configuration.json")):
+ with open(
+ os.path.join(model_or_path, "configuration.json"), "r", encoding="utf-8"
+ ) as f:
+ conf_json = json.load(f)
+ cfg = {}
+ add_file_root_path(model_or_path, conf_json["file_path_metas"], cfg)
+ cfg.update(kwargs)
+ config = OmegaConf.load(cfg["config"])
+ kwargs = OmegaConf.merge(config, cfg)
+ kwargs["model"] = config["model"]
+ elif os.path.exists(os.path.join(model_or_path, "config.yaml")) and os.path.exists(
+ os.path.join(model_or_path, "model.pt")
+ ):
+ config = OmegaConf.load(os.path.join(model_or_path, "config.yaml"))
+ kwargs = OmegaConf.merge(config, kwargs)
+ init_param = os.path.join(model_or_path, "model.pb")
+ kwargs["init_param"] = init_param
+ if os.path.exists(os.path.join(model_or_path, "tokens.txt")):
+ kwargs["tokenizer_conf"]["token_list"] = os.path.join(
+ model_or_path, "tokens.txt"
+ )
+ if os.path.exists(os.path.join(model_or_path, "tokens.json")):
+ kwargs["tokenizer_conf"]["token_list"] = os.path.join(
+ model_or_path, "tokens.json"
+ )
+ if os.path.exists(os.path.join(model_or_path, "seg_dict")):
+ kwargs["tokenizer_conf"]["seg_dict"] = os.path.join(
+ model_or_path, "seg_dict"
+ )
+ if os.path.exists(os.path.join(model_or_path, "bpe.model")):
+ kwargs["tokenizer_conf"]["bpemodel"] = os.path.join(
+ model_or_path, "bpe.model"
+ )
+ kwargs["model"] = config["model"]
+ if os.path.exists(os.path.join(model_or_path, "am.mvn")):
+ kwargs["frontend_conf"]["cmvn_file"] = os.path.join(model_or_path, "am.mvn")
+ if os.path.exists(os.path.join(model_or_path, "jieba_usr_dict")):
+ kwargs["jieba_usr_dict"] = os.path.join(model_or_path, "jieba_usr_dict")
+ return OmegaConf.to_container(kwargs, resolve=True)
+
+
+def add_file_root_path(model_or_path: str, file_path_metas: dict, cfg={}):
+
+ if isinstance(file_path_metas, dict):
+ for k, v in file_path_metas.items():
+ if isinstance(v, str):
+ p = os.path.join(model_or_path, v)
+ if os.path.exists(p):
+ cfg[k] = p
+ elif isinstance(v, dict):
+ if k not in cfg:
+ cfg[k] = {}
+ add_file_root_path(model_or_path, v, cfg[k])
+
+ return cfg
+
+
+def get_or_download_model_dir(
+ model,
+ model_revision=None,
+ is_training=False,
+ check_latest=True,
+):
+ """Get local model directory or download model if necessary.
+
+ Args:
+ model (str): model id or path to local model directory.
+ model_revision (str, optional): model version number.
+ :param is_training:
+ """
+ from modelscope.hub.check_model import check_local_model_is_latest
+ from modelscope.hub.snapshot_download import snapshot_download
+
+ from modelscope.utils.constant import Invoke, ThirdParty
+
+ key = Invoke.LOCAL_TRAINER if is_training else Invoke.PIPELINE
+
+ if os.path.exists(model) and check_latest:
+ model_cache_dir = model if os.path.isdir(model) else os.path.dirname(model)
+ try:
+ check_local_model_is_latest(
+ model_cache_dir, user_agent={Invoke.KEY: key, ThirdParty.KEY: "funasr"}
+ )
+ except:
+ print("could not check the latest version")
+ else:
+ model_cache_dir = snapshot_download(
+ model,
+ revision=model_revision,
+ user_agent={Invoke.KEY: key, ThirdParty.KEY: "funasr"},
+ )
+ return model_cache_dir
diff --git a/almeval/models/stepaudio/funasr_detach/download/file.py b/almeval/models/stepaudio/funasr_detach/download/file.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebb1a3dc10b7ef9385694e892c676847812adaab
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/download/file.py
@@ -0,0 +1,335 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+import contextlib
+import os
+import tempfile
+from abc import ABCMeta, abstractmethod
+from pathlib import Path
+from typing import Generator, Union
+
+import requests
+from urllib.parse import urlparse
+
+
+def download_from_url(url):
+ result = urlparse(url)
+ file_path = None
+ if result.scheme is not None and len(result.scheme) > 0:
+ storage = HTTPStorage()
+ # bytes
+ data = storage.read(url)
+ work_dir = tempfile.TemporaryDirectory().name
+ if not os.path.exists(work_dir):
+ os.makedirs(work_dir)
+ file_path = os.path.join(work_dir, os.path.basename(url))
+ with open(file_path, "wb") as fb:
+ fb.write(data)
+ assert file_path is not None, f"failed to download: {url}"
+ return file_path
+
+
+class Storage(metaclass=ABCMeta):
+ """Abstract class of storage.
+
+ All backends need to implement two apis: ``read()`` and ``read_text()``.
+ ``read()`` reads the file as a byte stream and ``read_text()`` reads
+ the file as texts.
+ """
+
+ @abstractmethod
+ def read(self, filepath: str):
+ pass
+
+ @abstractmethod
+ def read_text(self, filepath: str):
+ pass
+
+ @abstractmethod
+ def write(self, obj: bytes, filepath: Union[str, Path]) -> None:
+ pass
+
+ @abstractmethod
+ def write_text(
+ self, obj: str, filepath: Union[str, Path], encoding: str = "utf-8"
+ ) -> None:
+ pass
+
+
+class LocalStorage(Storage):
+ """Local hard disk storage"""
+
+ def read(self, filepath: Union[str, Path]) -> bytes:
+ """Read data from a given ``filepath`` with 'rb' mode.
+
+ Args:
+ filepath (str or Path): Path to read data.
+
+ Returns:
+ bytes: Expected bytes object.
+ """
+ with open(filepath, "rb") as f:
+ content = f.read()
+ return content
+
+ def read_text(self, filepath: Union[str, Path], encoding: str = "utf-8") -> str:
+ """Read data from a given ``filepath`` with 'r' mode.
+
+ Args:
+ filepath (str or Path): Path to read data.
+ encoding (str): The encoding format used to open the ``filepath``.
+ Default: 'utf-8'.
+
+ Returns:
+ str: Expected text reading from ``filepath``.
+ """
+ with open(filepath, "r", encoding=encoding) as f:
+ value_buf = f.read()
+ return value_buf
+
+ def write(self, obj: bytes, filepath: Union[str, Path]) -> None:
+ """Write data to a given ``filepath`` with 'wb' mode.
+
+ Note:
+ ``write`` will create a directory if the directory of ``filepath``
+ does not exist.
+
+ Args:
+ obj (bytes): Data to be written.
+ filepath (str or Path): Path to write data.
+ """
+ dirname = os.path.dirname(filepath)
+ if dirname and not os.path.exists(dirname):
+ os.makedirs(dirname, exist_ok=True)
+
+ with open(filepath, "wb") as f:
+ f.write(obj)
+
+ def write_text(
+ self, obj: str, filepath: Union[str, Path], encoding: str = "utf-8"
+ ) -> None:
+ """Write data to a given ``filepath`` with 'w' mode.
+
+ Note:
+ ``write_text`` will create a directory if the directory of
+ ``filepath`` does not exist.
+
+ Args:
+ obj (str): Data to be written.
+ filepath (str or Path): Path to write data.
+ encoding (str): The encoding format used to open the ``filepath``.
+ Default: 'utf-8'.
+ """
+ dirname = os.path.dirname(filepath)
+ if dirname and not os.path.exists(dirname):
+ os.makedirs(dirname, exist_ok=True)
+
+ with open(filepath, "w", encoding=encoding) as f:
+ f.write(obj)
+
+ @contextlib.contextmanager
+ def as_local_path(
+ self, filepath: Union[str, Path]
+ ) -> Generator[Union[str, Path], None, None]:
+ """Only for unified API and do nothing."""
+ yield filepath
+
+
+class HTTPStorage(Storage):
+ """HTTP and HTTPS storage."""
+
+ def read(self, url):
+ # TODO @wenmeng.zwm add progress bar if file is too large
+ r = requests.get(url)
+ r.raise_for_status()
+ return r.content
+
+ def read_text(self, url):
+ r = requests.get(url)
+ r.raise_for_status()
+ return r.text
+
+ @contextlib.contextmanager
+ def as_local_path(self, filepath: str) -> Generator[Union[str, Path], None, None]:
+ """Download a file from ``filepath``.
+
+ ``as_local_path`` is decorated by :meth:`contextlib.contextmanager`. It
+ can be called with ``with`` statement, and when exists from the
+ ``with`` statement, the temporary path will be released.
+
+ Args:
+ filepath (str): Download a file from ``filepath``.
+
+ Examples:
+ >>> storage = HTTPStorage()
+ >>> # After existing from the ``with`` clause,
+ >>> # the path will be removed
+ >>> with storage.get_local_path('http://path/to/file') as path:
+ ... # do something here
+ """
+ try:
+ f = tempfile.NamedTemporaryFile(delete=False)
+ f.write(self.read(filepath))
+ f.close()
+ yield f.name
+ finally:
+ os.remove(f.name)
+
+ def write(self, obj: bytes, url: Union[str, Path]) -> None:
+ raise NotImplementedError("write is not supported by HTTP Storage")
+
+ def write_text(
+ self, obj: str, url: Union[str, Path], encoding: str = "utf-8"
+ ) -> None:
+ raise NotImplementedError("write_text is not supported by HTTP Storage")
+
+
+class OSSStorage(Storage):
+ """OSS storage."""
+
+ def __init__(self, oss_config_file=None):
+ # read from config file or env var
+ raise NotImplementedError("OSSStorage.__init__ to be implemented in the future")
+
+ def read(self, filepath):
+ raise NotImplementedError("OSSStorage.read to be implemented in the future")
+
+ def read_text(self, filepath, encoding="utf-8"):
+ raise NotImplementedError(
+ "OSSStorage.read_text to be implemented in the future"
+ )
+
+ @contextlib.contextmanager
+ def as_local_path(self, filepath: str) -> Generator[Union[str, Path], None, None]:
+ """Download a file from ``filepath``.
+
+ ``as_local_path`` is decorated by :meth:`contextlib.contextmanager`. It
+ can be called with ``with`` statement, and when exists from the
+ ``with`` statement, the temporary path will be released.
+
+ Args:
+ filepath (str): Download a file from ``filepath``.
+
+ Examples:
+ >>> storage = OSSStorage()
+ >>> # After existing from the ``with`` clause,
+ >>> # the path will be removed
+ >>> with storage.get_local_path('http://path/to/file') as path:
+ ... # do something here
+ """
+ try:
+ f = tempfile.NamedTemporaryFile(delete=False)
+ f.write(self.read(filepath))
+ f.close()
+ yield f.name
+ finally:
+ os.remove(f.name)
+
+ def write(self, obj: bytes, filepath: Union[str, Path]) -> None:
+ raise NotImplementedError("OSSStorage.write to be implemented in the future")
+
+ def write_text(
+ self, obj: str, filepath: Union[str, Path], encoding: str = "utf-8"
+ ) -> None:
+ raise NotImplementedError(
+ "OSSStorage.write_text to be implemented in the future"
+ )
+
+
+G_STORAGES = {}
+
+
+class File(object):
+ _prefix_to_storage: dict = {
+ "oss": OSSStorage,
+ "http": HTTPStorage,
+ "https": HTTPStorage,
+ "local": LocalStorage,
+ }
+
+ @staticmethod
+ def _get_storage(uri):
+ assert isinstance(uri, str), f"uri should be str type, but got {type(uri)}"
+
+ if "://" not in uri:
+ # local path
+ storage_type = "local"
+ else:
+ prefix, _ = uri.split("://")
+ storage_type = prefix
+
+ assert storage_type in File._prefix_to_storage, (
+ f"Unsupported uri {uri}, valid prefixs: "
+ f"{list(File._prefix_to_storage.keys())}"
+ )
+
+ if storage_type not in G_STORAGES:
+ G_STORAGES[storage_type] = File._prefix_to_storage[storage_type]()
+
+ return G_STORAGES[storage_type]
+
+ @staticmethod
+ def read(uri: str) -> bytes:
+ """Read data from a given ``filepath`` with 'rb' mode.
+
+ Args:
+ filepath (str or Path): Path to read data.
+
+ Returns:
+ bytes: Expected bytes object.
+ """
+ storage = File._get_storage(uri)
+ return storage.read(uri)
+
+ @staticmethod
+ def read_text(uri: Union[str, Path], encoding: str = "utf-8") -> str:
+ """Read data from a given ``filepath`` with 'r' mode.
+
+ Args:
+ filepath (str or Path): Path to read data.
+ encoding (str): The encoding format used to open the ``filepath``.
+ Default: 'utf-8'.
+
+ Returns:
+ str: Expected text reading from ``filepath``.
+ """
+ storage = File._get_storage(uri)
+ return storage.read_text(uri)
+
+ @staticmethod
+ def write(obj: bytes, uri: Union[str, Path]) -> None:
+ """Write data to a given ``filepath`` with 'wb' mode.
+
+ Note:
+ ``write`` will create a directory if the directory of ``filepath``
+ does not exist.
+
+ Args:
+ obj (bytes): Data to be written.
+ filepath (str or Path): Path to write data.
+ """
+ storage = File._get_storage(uri)
+ return storage.write(obj, uri)
+
+ @staticmethod
+ def write_text(obj: str, uri: str, encoding: str = "utf-8") -> None:
+ """Write data to a given ``filepath`` with 'w' mode.
+
+ Note:
+ ``write_text`` will create a directory if the directory of
+ ``filepath`` does not exist.
+
+ Args:
+ obj (str): Data to be written.
+ filepath (str or Path): Path to write data.
+ encoding (str): The encoding format used to open the ``filepath``.
+ Default: 'utf-8'.
+ """
+ storage = File._get_storage(uri)
+ return storage.write_text(obj, uri)
+
+ @contextlib.contextmanager
+ def as_local_path(uri: str) -> Generator[Union[str, Path], None, None]:
+ """Only for unified API and do nothing."""
+ storage = File._get_storage(uri)
+ with storage.as_local_path(uri) as local_path:
+ yield local_path
diff --git a/almeval/models/stepaudio/funasr_detach/download/name_maps_from_hub.py b/almeval/models/stepaudio/funasr_detach/download/name_maps_from_hub.py
new file mode 100644
index 0000000000000000000000000000000000000000..695d247374ef6e2bdacb78adadbdeadf451f93ca
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/download/name_maps_from_hub.py
@@ -0,0 +1,13 @@
+name_maps_ms = {
+ "paraformer-zh": "damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+ "paraformer-en": "damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020",
+ "paraformer-en-spk": "damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020",
+ "paraformer-zh-streaming": "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online",
+ "fsmn-vad": "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+ "ct-punc": "damo/punc_ct-transformer_cn-en-common-vocab471067-large",
+ "ct-punc-c": "damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
+ "fa-zh": "damo/speech_timestamp_prediction-v1-16k-offline",
+ "cam++": "damo/speech_campplus_sv_zh-cn_16k-common",
+}
+
+name_maps_hf = {}
diff --git a/almeval/models/stepaudio/funasr_detach/download/runtime_sdk_download_tool.py b/almeval/models/stepaudio/funasr_detach/download/runtime_sdk_download_tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc7e8e465aed348d25cbdd015095271fb7434cd3
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/download/runtime_sdk_download_tool.py
@@ -0,0 +1,60 @@
+import os
+import argparse
+from pathlib import Path
+
+from funasr_detach.utils.types import str2bool
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--model-name", type=str, required=True)
+ parser.add_argument("--export-dir", type=str, required=True)
+ parser.add_argument(
+ "--export", type=str2bool, default=True, help="whether to export model"
+ )
+ parser.add_argument("--type", type=str, default="onnx", help='["onnx", "torch"]')
+ parser.add_argument("--device", type=str, default="cpu", help='["cpu", "cuda"]')
+ parser.add_argument(
+ "--quantize", type=str2bool, default=False, help="export quantized model"
+ )
+ parser.add_argument(
+ "--fallback-num", type=int, default=0, help="amp fallback number"
+ )
+ parser.add_argument("--audio_in", type=str, default=None, help='["wav", "wav.scp"]')
+ parser.add_argument(
+ "--model_revision", type=str, default=None, help="model_revision"
+ )
+ parser.add_argument("--calib_num", type=int, default=200, help="calib max num")
+ args = parser.parse_args()
+
+ model_dir = args.model_name
+ if not Path(args.model_name).exists():
+ from modelscope.hub.snapshot_download import snapshot_download
+
+ try:
+ model_dir = snapshot_download(
+ args.model_name, cache_dir=args.export_dir, revision=args.model_revision
+ )
+ except:
+ raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format(
+ model_dir
+ )
+ if args.export:
+ model_file = os.path.join(model_dir, "model.onnx")
+ if args.quantize:
+ model_file = os.path.join(model_dir, "model_quant.onnx")
+ if not os.path.exists(model_file):
+ print(".onnx is not exist, begin to export onnx")
+ from funasr_detach.bin.export_model import ModelExport
+
+ export_model = ModelExport(
+ cache_dir=args.export_dir,
+ onnx=True,
+ device="cpu",
+ quant=args.quantize,
+ )
+ export_model.export(model_dir)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/almeval/models/stepaudio/funasr_detach/frontends/__init__.py b/almeval/models/stepaudio/funasr_detach/frontends/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/almeval/models/stepaudio/funasr_detach/frontends/default.py b/almeval/models/stepaudio/funasr_detach/frontends/default.py
new file mode 100644
index 0000000000000000000000000000000000000000..80ba539b1d06d8d3fd50bdc021b7ad7e1dd7bc82
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/frontends/default.py
@@ -0,0 +1,347 @@
+import copy
+from typing import Optional
+from typing import Tuple
+from typing import Union
+import logging
+import humanfriendly
+import numpy as np
+import torch
+import torch.nn as nn
+
+try:
+ from torch_complex.tensor import ComplexTensor
+except:
+ print("Please install torch_complex firstly")
+
+from funasr_detach.frontends.utils.log_mel import LogMel
+from funasr_detach.frontends.utils.stft import Stft
+from funasr_detach.frontends.utils.frontend import Frontend
+from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask
+
+
+class DefaultFrontend(nn.Module):
+ """Conventional frontend structure for ASR.
+ Stft -> WPE -> MVDR-Beamformer -> Power-spec -> Mel-Fbank -> CMVN
+ """
+
+ def __init__(
+ self,
+ fs: Union[int, str] = 16000,
+ n_fft: int = 512,
+ win_length: int = None,
+ hop_length: int = 128,
+ window: Optional[str] = "hann",
+ center: bool = True,
+ normalized: bool = False,
+ onesided: bool = True,
+ n_mels: int = 80,
+ fmin: int = None,
+ fmax: int = None,
+ htk: bool = False,
+ frontend_conf: Optional[dict] = None,
+ apply_stft: bool = True,
+ use_channel: int = None,
+ ):
+ super().__init__()
+ if isinstance(fs, str):
+ fs = humanfriendly.parse_size(fs)
+
+ # Deepcopy (In general, dict shouldn't be used as default arg)
+ frontend_conf = copy.deepcopy(frontend_conf)
+ self.hop_length = hop_length
+
+ if apply_stft:
+ self.stft = Stft(
+ n_fft=n_fft,
+ win_length=win_length,
+ hop_length=hop_length,
+ center=center,
+ window=window,
+ normalized=normalized,
+ onesided=onesided,
+ )
+ else:
+ self.stft = None
+ self.apply_stft = apply_stft
+
+ if frontend_conf is not None:
+ self.frontend = Frontend(idim=n_fft // 2 + 1, **frontend_conf)
+ else:
+ self.frontend = None
+
+ self.logmel = LogMel(
+ fs=fs,
+ n_fft=n_fft,
+ n_mels=n_mels,
+ fmin=fmin,
+ fmax=fmax,
+ htk=htk,
+ )
+ self.n_mels = n_mels
+ self.use_channel = use_channel
+ self.frontend_type = "default"
+
+ def output_size(self) -> int:
+ return self.n_mels
+
+ def forward(
+ self, input: torch.Tensor, input_lengths: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ # 1. Domain-conversion: e.g. Stft: time -> time-freq
+ if self.stft is not None:
+ input_stft, feats_lens = self._compute_stft(input, input_lengths)
+ else:
+ input_stft = ComplexTensor(input[..., 0], input[..., 1])
+ feats_lens = input_lengths
+ # 2. [Option] Speech enhancement
+ if self.frontend is not None:
+ assert isinstance(input_stft, ComplexTensor), type(input_stft)
+ # input_stft: (Batch, Length, [Channel], Freq)
+ input_stft, _, mask = self.frontend(input_stft, feats_lens)
+
+ # 3. [Multi channel case]: Select a channel
+ if input_stft.dim() == 4:
+ # h: (B, T, C, F) -> h: (B, T, F)
+ if self.training:
+ if self.use_channel is not None:
+ input_stft = input_stft[:, :, self.use_channel, :]
+ else:
+ # Select 1ch randomly
+ ch = np.random.randint(input_stft.size(2))
+ input_stft = input_stft[:, :, ch, :]
+ else:
+ # Use the first channel
+ input_stft = input_stft[:, :, 0, :]
+
+ # 4. STFT -> Power spectrum
+ # h: ComplexTensor(B, T, F) -> torch.Tensor(B, T, F)
+ input_power = input_stft.real**2 + input_stft.imag**2
+
+ # 5. Feature transform e.g. Stft -> Log-Mel-Fbank
+ # input_power: (Batch, [Channel,] Length, Freq)
+ # -> input_feats: (Batch, Length, Dim)
+ input_feats, _ = self.logmel(input_power, feats_lens)
+
+ return input_feats, feats_lens
+
+ def _compute_stft(
+ self, input: torch.Tensor, input_lengths: torch.Tensor
+ ) -> torch.Tensor:
+ input_stft, feats_lens = self.stft(input, input_lengths)
+
+ assert input_stft.dim() >= 4, input_stft.shape
+ # "2" refers to the real/imag parts of Complex
+ assert input_stft.shape[-1] == 2, input_stft.shape
+
+ # Change torch.Tensor to ComplexTensor
+ # input_stft: (..., F, 2) -> (..., F)
+ input_stft = ComplexTensor(input_stft[..., 0], input_stft[..., 1])
+ return input_stft, feats_lens
+
+
+class MultiChannelFrontend(nn.Module):
+ """Conventional frontend structure for ASR.
+ Stft -> WPE -> MVDR-Beamformer -> Power-spec -> Mel-Fbank -> CMVN
+ """
+
+ def __init__(
+ self,
+ fs: Union[int, str] = 16000,
+ n_fft: int = 512,
+ win_length: int = None,
+ hop_length: int = None,
+ frame_length: int = None,
+ frame_shift: int = None,
+ window: Optional[str] = "hann",
+ center: bool = True,
+ normalized: bool = False,
+ onesided: bool = True,
+ n_mels: int = 80,
+ fmin: int = None,
+ fmax: int = None,
+ htk: bool = False,
+ frontend_conf: Optional[dict] = None,
+ apply_stft: bool = True,
+ use_channel: int = None,
+ lfr_m: int = 1,
+ lfr_n: int = 1,
+ cmvn_file: str = None,
+ mc: bool = True,
+ ):
+ super().__init__()
+ if isinstance(fs, str):
+ fs = humanfriendly.parse_size(fs)
+
+ # Deepcopy (In general, dict shouldn't be used as default arg)
+ frontend_conf = copy.deepcopy(frontend_conf)
+ if win_length is None and hop_length is None:
+ self.win_length = frame_length * 16
+ self.hop_length = frame_shift * 16
+ elif frame_length is None and frame_shift is None:
+ self.win_length = self.win_length
+ self.hop_length = self.hop_length
+ else:
+ logging.error(
+ "Only one of (win_length, hop_length) and (frame_length, frame_shift)"
+ "can be set."
+ )
+ exit(1)
+
+ if apply_stft:
+ self.stft = Stft(
+ n_fft=n_fft,
+ win_length=self.win_length,
+ hop_length=self.hop_length,
+ center=center,
+ window=window,
+ normalized=normalized,
+ onesided=onesided,
+ )
+ else:
+ self.stft = None
+ self.apply_stft = apply_stft
+
+ if frontend_conf is not None:
+ self.frontend = Frontend(idim=n_fft // 2 + 1, **frontend_conf)
+ else:
+ self.frontend = None
+
+ self.logmel = LogMel(
+ fs=fs,
+ n_fft=n_fft,
+ n_mels=n_mels,
+ fmin=fmin,
+ fmax=fmax,
+ htk=htk,
+ )
+ self.n_mels = n_mels
+ self.use_channel = use_channel
+ self.mc = mc
+ if not self.mc:
+ if self.use_channel is not None:
+ logging.info("use the channel %d" % (self.use_channel))
+ else:
+ logging.info("random select channel")
+ self.cmvn_file = cmvn_file
+ if self.cmvn_file is not None:
+ mean, std = self._load_cmvn(self.cmvn_file)
+ self.register_buffer("mean", torch.from_numpy(mean))
+ self.register_buffer("std", torch.from_numpy(std))
+ self.frontend_type = "multichannelfrontend"
+
+ def output_size(self) -> int:
+ return self.n_mels
+
+ def forward(
+ self, input: torch.Tensor, input_lengths: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ # 1. Domain-conversion: e.g. Stft: time -> time-freq
+ # import pdb;pdb.set_trace()
+ if self.stft is not None:
+ input_stft, feats_lens = self._compute_stft(input, input_lengths)
+ else:
+ input_stft = ComplexTensor(input[..., 0], input[..., 1])
+ feats_lens = input_lengths
+ # 2. [Option] Speech enhancement
+ if self.frontend is not None:
+ assert isinstance(input_stft, ComplexTensor), type(input_stft)
+ # input_stft: (Batch, Length, [Channel], Freq)
+ input_stft, _, mask = self.frontend(input_stft, feats_lens)
+
+ # 3. [Multi channel case]: Select a channel(sa_asr)
+ if input_stft.dim() == 4 and not self.mc:
+ # h: (B, T, C, F) -> h: (B, T, F)
+ if self.training:
+ if self.use_channel is not None:
+ input_stft = input_stft[:, :, self.use_channel, :]
+
+ else:
+ # Select 1ch randomly
+ ch = np.random.randint(input_stft.size(2))
+ input_stft = input_stft[:, :, ch, :]
+ else:
+ # Use the first channel
+ input_stft = input_stft[:, :, 0, :]
+
+ # 4. STFT -> Power spectrum
+ # h: ComplexTensor(B, T, F) -> torch.Tensor(B, T, F)
+ input_power = input_stft.real**2 + input_stft.imag**2
+
+ # 5. Feature transform e.g. Stft -> Log-Mel-Fbank
+ # input_power: (Batch, [Channel,] Length, Freq)
+ # -> input_feats: (Batch, Length, Dim)
+ input_feats, _ = self.logmel(input_power, feats_lens)
+ if self.mc:
+ # MFCCA
+ if input_feats.dim() == 4:
+ bt = input_feats.size(0)
+ channel_size = input_feats.size(2)
+ input_feats = (
+ input_feats.transpose(1, 2)
+ .reshape(bt * channel_size, -1, 80)
+ .contiguous()
+ )
+ feats_lens = feats_lens.repeat(1, channel_size).squeeze()
+ else:
+ channel_size = 1
+ return input_feats, feats_lens, channel_size
+ else:
+ # 6. Apply CMVN
+ if self.cmvn_file is not None:
+ if feats_lens is None:
+ feats_lens = input_feats.new_full(
+ [input_feats.size(0)], input_feats.size(1)
+ )
+ self.mean = self.mean.to(input_feats.device, input_feats.dtype)
+ self.std = self.std.to(input_feats.device, input_feats.dtype)
+ mask = make_pad_mask(feats_lens, input_feats, 1)
+
+ if input_feats.requires_grad:
+ input_feats = input_feats + self.mean
+ else:
+ input_feats += self.mean
+ if input_feats.requires_grad:
+ input_feats = input_feats.masked_fill(mask, 0.0)
+ else:
+ input_feats.masked_fill_(mask, 0.0)
+
+ input_feats *= self.std
+
+ return input_feats, feats_lens
+
+ def _compute_stft(
+ self, input: torch.Tensor, input_lengths: torch.Tensor
+ ) -> torch.Tensor:
+ input_stft, feats_lens = self.stft(input, input_lengths)
+
+ assert input_stft.dim() >= 4, input_stft.shape
+ # "2" refers to the real/imag parts of Complex
+ assert input_stft.shape[-1] == 2, input_stft.shape
+
+ # Change torch.Tensor to ComplexTensor
+ # input_stft: (..., F, 2) -> (..., F)
+ input_stft = ComplexTensor(input_stft[..., 0], input_stft[..., 1])
+ return input_stft, feats_lens
+
+ def _load_cmvn(self, cmvn_file):
+ with open(cmvn_file, "r", encoding="utf-8") as f:
+ lines = f.readlines()
+ means_list = []
+ vars_list = []
+ for i in range(len(lines)):
+ line_item = lines[i].split()
+ if line_item[0] == "":
+ line_item = lines[i + 1].split()
+ if line_item[0] == "":
+ add_shift_line = line_item[3 : (len(line_item) - 1)]
+ means_list = list(add_shift_line)
+ continue
+ elif line_item[0] == "":
+ line_item = lines[i + 1].split()
+ if line_item[0] == "":
+ rescale_line = line_item[3 : (len(line_item) - 1)]
+ vars_list = list(rescale_line)
+ continue
+ means = np.array(means_list).astype(np.float)
+ vars = np.array(vars_list).astype(np.float)
+ return means, vars
diff --git a/almeval/models/stepaudio/funasr_detach/frontends/eend_ola_feature.py b/almeval/models/stepaudio/funasr_detach/frontends/eend_ola_feature.py
new file mode 100644
index 0000000000000000000000000000000000000000..13438175867c9795cc349200dae754b9282c81b9
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/frontends/eend_ola_feature.py
@@ -0,0 +1,49 @@
+# Copyright 2019 Hitachi, Ltd. (author: Yusuke Fujita)
+# Licensed under the MIT license.
+#
+# This module is for computing audio features
+
+import librosa
+import numpy as np
+
+
+def transform(Y, dtype=np.float32):
+ Y = np.abs(Y)
+ n_fft = 2 * (Y.shape[1] - 1)
+ sr = 8000
+ n_mels = 23
+ mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
+ Y = np.dot(Y**2, mel_basis.T)
+ Y = np.log10(np.maximum(Y, 1e-10))
+ mean = np.mean(Y, axis=0)
+ Y = Y - mean
+ return Y.astype(dtype)
+
+
+def subsample(Y, T, subsampling=1):
+ Y_ss = Y[::subsampling]
+ T_ss = T[::subsampling]
+ return Y_ss, T_ss
+
+
+def splice(Y, context_size=0):
+ Y_pad = np.pad(Y, [(context_size, context_size), (0, 0)], "constant")
+ Y_spliced = np.lib.stride_tricks.as_strided(
+ np.ascontiguousarray(Y_pad),
+ (Y.shape[0], Y.shape[1] * (2 * context_size + 1)),
+ (Y.itemsize * Y.shape[1], Y.itemsize),
+ writeable=False,
+ )
+ return Y_spliced
+
+
+def stft(data, frame_size=1024, frame_shift=256):
+ fft_size = 1 << (frame_size - 1).bit_length()
+ if len(data) % frame_shift == 0:
+ return librosa.stft(
+ data, n_fft=fft_size, win_length=frame_size, hop_length=frame_shift
+ ).T[:-1]
+ else:
+ return librosa.stft(
+ data, n_fft=fft_size, win_length=frame_size, hop_length=frame_shift
+ ).T
diff --git a/almeval/models/stepaudio/funasr_detach/frontends/fused.py b/almeval/models/stepaudio/funasr_detach/frontends/fused.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd4eeb10af985154d1bd192a8ad7b92ed3d4a383
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/frontends/fused.py
@@ -0,0 +1,144 @@
+from funasr_detach.frontends.default import DefaultFrontend
+from funasr_detach.frontends.s3prl import S3prlFrontend
+import numpy as np
+import torch
+import torch.nn as nn
+from typing import Tuple
+
+
+class FusedFrontends(nn.Module):
+ def __init__(
+ self, frontends=None, align_method="linear_projection", proj_dim=100, fs=16000
+ ):
+
+ super().__init__()
+ self.align_method = (
+ align_method # fusing method : linear_projection only for now
+ )
+ self.proj_dim = proj_dim # dim of the projection done on each frontend
+ self.frontends = [] # list of the frontends to combine
+
+ for i, frontend in enumerate(frontends):
+ frontend_type = frontend["frontend_type"]
+ if frontend_type == "default":
+ n_mels, fs, n_fft, win_length, hop_length = (
+ frontend.get("n_mels", 80),
+ fs,
+ frontend.get("n_fft", 512),
+ frontend.get("win_length"),
+ frontend.get("hop_length", 128),
+ )
+ window, center, normalized, onesided = (
+ frontend.get("window", "hann"),
+ frontend.get("center", True),
+ frontend.get("normalized", False),
+ frontend.get("onesided", True),
+ )
+ fmin, fmax, htk, apply_stft = (
+ frontend.get("fmin", None),
+ frontend.get("fmax", None),
+ frontend.get("htk", False),
+ frontend.get("apply_stft", True),
+ )
+
+ self.frontends.append(
+ DefaultFrontend(
+ n_mels=n_mels,
+ n_fft=n_fft,
+ fs=fs,
+ win_length=win_length,
+ hop_length=hop_length,
+ window=window,
+ center=center,
+ normalized=normalized,
+ onesided=onesided,
+ fmin=fmin,
+ fmax=fmax,
+ htk=htk,
+ apply_stft=apply_stft,
+ )
+ )
+ elif frontend_type == "s3prl":
+ frontend_conf, download_dir, multilayer_feature = (
+ frontend.get("frontend_conf"),
+ frontend.get("download_dir"),
+ frontend.get("multilayer_feature"),
+ )
+ self.frontends.append(
+ S3prlFrontend(
+ fs=fs,
+ frontend_conf=frontend_conf,
+ download_dir=download_dir,
+ multilayer_feature=multilayer_feature,
+ )
+ )
+
+ else:
+ raise NotImplementedError # frontends are only default or s3prl
+
+ self.frontends = torch.nn.ModuleList(self.frontends)
+
+ self.gcd = np.gcd.reduce([frontend.hop_length for frontend in self.frontends])
+ self.factors = [frontend.hop_length // self.gcd for frontend in self.frontends]
+ if torch.cuda.is_available():
+ dev = "cuda"
+ else:
+ dev = "cpu"
+ if self.align_method == "linear_projection":
+ self.projection_layers = [
+ torch.nn.Linear(
+ in_features=frontend.output_size(),
+ out_features=self.factors[i] * self.proj_dim,
+ )
+ for i, frontend in enumerate(self.frontends)
+ ]
+ self.projection_layers = torch.nn.ModuleList(self.projection_layers)
+ self.projection_layers = self.projection_layers.to(torch.device(dev))
+
+ def output_size(self) -> int:
+ return len(self.frontends) * self.proj_dim
+
+ def forward(
+ self, input: torch.Tensor, input_lengths: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+
+ # step 0 : get all frontends features
+ self.feats = []
+ for frontend in self.frontends:
+ with torch.no_grad():
+ input_feats, feats_lens = frontend.forward(input, input_lengths)
+ self.feats.append([input_feats, feats_lens])
+
+ if (
+ self.align_method == "linear_projection"
+ ): # TODO(Dan): to add other align methods
+
+ # first step : projections
+ self.feats_proj = []
+ for i, frontend in enumerate(self.frontends):
+ input_feats = self.feats[i][0]
+ self.feats_proj.append(self.projection_layers[i](input_feats))
+
+ # 2nd step : reshape
+ self.feats_reshaped = []
+ for i, frontend in enumerate(self.frontends):
+ input_feats_proj = self.feats_proj[i]
+ bs, nf, dim = input_feats_proj.shape
+ input_feats_reshaped = torch.reshape(
+ input_feats_proj, (bs, nf * self.factors[i], dim // self.factors[i])
+ )
+ self.feats_reshaped.append(input_feats_reshaped)
+
+ # 3rd step : drop the few last frames
+ m = min([x.shape[1] for x in self.feats_reshaped])
+ self.feats_final = [x[:, :m, :] for x in self.feats_reshaped]
+
+ input_feats = torch.cat(
+ self.feats_final, dim=-1
+ ) # change the input size of the preencoder : proj_dim * n_frontends
+ feats_lens = torch.ones_like(self.feats[0][1]) * (m)
+
+ else:
+ raise NotImplementedError
+
+ return input_feats, feats_lens
diff --git a/almeval/models/stepaudio/funasr_detach/frontends/s3prl.py b/almeval/models/stepaudio/funasr_detach/frontends/s3prl.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4c759fd740245bc695bca72fa196aba8e49c007
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/frontends/s3prl.py
@@ -0,0 +1,139 @@
+import copy
+import logging
+import os
+from argparse import Namespace
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import humanfriendly
+import torch
+import torch.nn as nn
+
+from funasr_detach.frontends.utils.frontend import Frontend
+from funasr_detach.models.transformer.utils.nets_utils import pad_list
+
+
+def base_s3prl_setup(args):
+ args.upstream_feature_selection = getattr(args, "upstream_feature_selection", None)
+ args.upstream_model_config = getattr(args, "upstream_model_config", None)
+ args.upstream_refresh = getattr(args, "upstream_refresh", False)
+ args.upstream_ckpt = getattr(args, "upstream_ckpt", None)
+ args.init_ckpt = getattr(args, "init_ckpt", None)
+ args.verbose = getattr(args, "verbose", False)
+ args.tile_factor = getattr(args, "tile_factor", 1)
+ return args
+
+
+class S3prlFrontend(nn.Module):
+ """Speech Pretrained Representation frontend structure for ASR."""
+
+ def __init__(
+ self,
+ fs: Union[int, str] = 16000,
+ frontend_conf: Optional[dict] = None,
+ download_dir: str = None,
+ multilayer_feature: bool = False,
+ ):
+ super().__init__()
+ if isinstance(fs, str):
+ fs = humanfriendly.parse_size(fs)
+
+ if download_dir is not None:
+ torch.hub.set_dir(download_dir)
+
+ self.multilayer_feature = multilayer_feature
+ self.upstream, self.featurizer = self._get_upstream(frontend_conf)
+ self.pretrained_params = copy.deepcopy(self.upstream.state_dict())
+ self.output_dim = self.featurizer.output_dim
+ self.frontend_type = "s3prl"
+ self.hop_length = self.upstream.get_downsample_rates("key")
+
+ def _get_upstream(self, frontend_conf):
+ """Get S3PRL upstream model."""
+ s3prl_args = base_s3prl_setup(
+ Namespace(**frontend_conf, device="cpu"),
+ )
+ self.args = s3prl_args
+
+ s3prl_path = None
+ python_path_list = os.environ.get("PYTHONPATH", "(None)").split(":")
+ for p in python_path_list:
+ if p.endswith("s3prl"):
+ s3prl_path = p
+ break
+ assert s3prl_path is not None
+
+ s3prl_upstream = torch.hub.load(
+ s3prl_path,
+ s3prl_args.upstream,
+ ckpt=s3prl_args.upstream_ckpt,
+ model_config=s3prl_args.upstream_model_config,
+ refresh=s3prl_args.upstream_refresh,
+ source="local",
+ ).to("cpu")
+
+ if getattr(
+ s3prl_upstream, "model", None
+ ) is not None and s3prl_upstream.model.__class__.__name__ in [
+ "Wav2Vec2Model",
+ "HubertModel",
+ ]:
+ s3prl_upstream.model.encoder.layerdrop = 0.0
+
+ from s3prl.upstream.interfaces import Featurizer
+
+ if self.multilayer_feature is None:
+ feature_selection = "last_hidden_state"
+ else:
+ feature_selection = "hidden_states"
+ s3prl_featurizer = Featurizer(
+ upstream=s3prl_upstream,
+ feature_selection=feature_selection,
+ upstream_device="cpu",
+ )
+
+ return s3prl_upstream, s3prl_featurizer
+
+ def _tile_representations(self, feature):
+ """Tile up the representations by `tile_factor`.
+ Input - sequence of representations
+ shape: (batch_size, seq_len, feature_dim)
+ Output - sequence of tiled representations
+ shape: (batch_size, seq_len * factor, feature_dim)
+ """
+ assert (
+ len(feature.shape) == 3
+ ), "Input argument `feature` has invalid shape: {}".format(feature.shape)
+ tiled_feature = feature.repeat(1, 1, self.args.tile_factor)
+ tiled_feature = tiled_feature.reshape(
+ feature.size(0), feature.size(1) * self.args.tile_factor, feature.size(2)
+ )
+ return tiled_feature
+
+ def output_size(self) -> int:
+ return self.output_dim
+
+ def forward(
+ self, input: torch.Tensor, input_lengths: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ wavs = [wav[: input_lengths[i]] for i, wav in enumerate(input)]
+ self.upstream.eval()
+ with torch.no_grad():
+ feats = self.upstream(wavs)
+ feats = self.featurizer(wavs, feats)
+
+ if self.args.tile_factor != 1:
+ feats = self._tile_representations(feats)
+
+ input_feats = pad_list(feats, 0.0)
+ feats_lens = torch.tensor([f.shape[0] for f in feats], dtype=torch.long)
+
+ # Saving CUDA Memory
+ del feats
+
+ return input_feats, feats_lens
+
+ def reload_pretrained_parameters(self):
+ self.upstream.load_state_dict(self.pretrained_params)
+ logging.info("Pretrained S3PRL frontend model parameters reloaded!")
diff --git a/almeval/models/stepaudio/funasr_detach/frontends/utils/beamformer.py b/almeval/models/stepaudio/funasr_detach/frontends/utils/beamformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3eccee4cf98b164f8eb9802bde3741ac23dc9dc
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/frontends/utils/beamformer.py
@@ -0,0 +1,84 @@
+import torch
+from torch_complex import functional as FC
+from torch_complex.tensor import ComplexTensor
+
+
+def get_power_spectral_density_matrix(
+ xs: ComplexTensor, mask: torch.Tensor, normalization=True, eps: float = 1e-15
+) -> ComplexTensor:
+ """Return cross-channel power spectral density (PSD) matrix
+
+ Args:
+ xs (ComplexTensor): (..., F, C, T)
+ mask (torch.Tensor): (..., F, C, T)
+ normalization (bool):
+ eps (float):
+ Returns
+ psd (ComplexTensor): (..., F, C, C)
+
+ """
+ # outer product: (..., C_1, T) x (..., C_2, T) -> (..., T, C, C_2)
+ psd_Y = FC.einsum("...ct,...et->...tce", [xs, xs.conj()])
+
+ # Averaging mask along C: (..., C, T) -> (..., T)
+ mask = mask.mean(dim=-2)
+
+ # Normalized mask along T: (..., T)
+ if normalization:
+ # If assuming the tensor is padded with zero, the summation along
+ # the time axis is same regardless of the padding length.
+ mask = mask / (mask.sum(dim=-1, keepdim=True) + eps)
+
+ # psd: (..., T, C, C)
+ psd = psd_Y * mask[..., None, None]
+ # (..., T, C, C) -> (..., C, C)
+ psd = psd.sum(dim=-3)
+
+ return psd
+
+
+def get_mvdr_vector(
+ psd_s: ComplexTensor,
+ psd_n: ComplexTensor,
+ reference_vector: torch.Tensor,
+ eps: float = 1e-15,
+) -> ComplexTensor:
+ """Return the MVDR(Minimum Variance Distortionless Response) vector:
+
+ h = (Npsd^-1 @ Spsd) / (Tr(Npsd^-1 @ Spsd)) @ u
+
+ Reference:
+ On optimal frequency-domain multichannel linear filtering
+ for noise reduction; M. Souden et al., 2010;
+ https://ieeexplore.ieee.org/document/5089420
+
+ Args:
+ psd_s (ComplexTensor): (..., F, C, C)
+ psd_n (ComplexTensor): (..., F, C, C)
+ reference_vector (torch.Tensor): (..., C)
+ eps (float):
+ Returns:
+ beamform_vector (ComplexTensor)r: (..., F, C)
+ """
+ # Add eps
+ C = psd_n.size(-1)
+ eye = torch.eye(C, dtype=psd_n.dtype, device=psd_n.device)
+ shape = [1 for _ in range(psd_n.dim() - 2)] + [C, C]
+ eye = eye.view(*shape)
+ psd_n += eps * eye
+
+ # numerator: (..., C_1, C_2) x (..., C_2, C_3) -> (..., C_1, C_3)
+ numerator = FC.einsum("...ec,...cd->...ed", [psd_n.inverse(), psd_s])
+ # ws: (..., C, C) / (...,) -> (..., C, C)
+ ws = numerator / (FC.trace(numerator)[..., None, None] + eps)
+ # h: (..., F, C_1, C_2) x (..., C_2) -> (..., F, C_1)
+ beamform_vector = FC.einsum("...fec,...c->...fe", [ws, reference_vector])
+ return beamform_vector
+
+
+def apply_beamforming_vector(
+ beamform_vector: ComplexTensor, mix: ComplexTensor
+) -> ComplexTensor:
+ # (..., C) x (..., C, T) -> (..., T)
+ es = FC.einsum("...c,...ct->...t", [beamform_vector.conj(), mix])
+ return es
diff --git a/almeval/models/stepaudio/funasr_detach/frontends/wav_frontend.py b/almeval/models/stepaudio/funasr_detach/frontends/wav_frontend.py
new file mode 100644
index 0000000000000000000000000000000000000000..36210348b45dd878c11ea5d13b5d01073c49c807
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/frontends/wav_frontend.py
@@ -0,0 +1,556 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+# Part of the implementation is borrowed from espnet/espnet.
+from typing import Tuple
+import copy
+import numpy as np
+import torch
+import torch.nn as nn
+import torchaudio.compliance.kaldi as kaldi
+from torch.nn.utils.rnn import pad_sequence
+
+import funasr_detach.frontends.eend_ola_feature as eend_ola_feature
+from funasr_detach.register import tables
+
+
+def load_cmvn(cmvn_file):
+ with open(cmvn_file, "r", encoding="utf-8") as f:
+ lines = f.readlines()
+ means_list = []
+ vars_list = []
+ for i in range(len(lines)):
+ line_item = lines[i].split()
+ if line_item[0] == "":
+ line_item = lines[i + 1].split()
+ if line_item[0] == "":
+ add_shift_line = line_item[3 : (len(line_item) - 1)]
+ means_list = list(add_shift_line)
+ continue
+ elif line_item[0] == "":
+ line_item = lines[i + 1].split()
+ if line_item[0] == "":
+ rescale_line = line_item[3 : (len(line_item) - 1)]
+ vars_list = list(rescale_line)
+ continue
+ means = np.array(means_list).astype(np.float32)
+ vars = np.array(vars_list).astype(np.float32)
+ cmvn = np.array([means, vars])
+ cmvn = torch.as_tensor(cmvn, dtype=torch.float32)
+ return cmvn
+
+
+def apply_cmvn(inputs, cmvn): # noqa
+ """
+ Apply CMVN with mvn data
+ """
+
+ device = inputs.device
+ dtype = inputs.dtype
+ frame, dim = inputs.shape
+
+ means = cmvn[0:1, :dim]
+ vars = cmvn[1:2, :dim]
+ inputs += means.to(device)
+ inputs *= vars.to(device)
+
+ return inputs.type(torch.float32)
+
+
+def apply_lfr(inputs, lfr_m, lfr_n):
+ LFR_inputs = []
+ T = inputs.shape[0]
+ T_lfr = int(np.ceil(T / lfr_n))
+ left_padding = inputs[0].repeat((lfr_m - 1) // 2, 1)
+ inputs = torch.vstack((left_padding, inputs))
+ T = T + (lfr_m - 1) // 2
+ for i in range(T_lfr):
+ if lfr_m <= T - i * lfr_n:
+ LFR_inputs.append((inputs[i * lfr_n : i * lfr_n + lfr_m]).view(1, -1))
+ else: # process last LFR frame
+ num_padding = lfr_m - (T - i * lfr_n)
+ frame = (inputs[i * lfr_n :]).view(-1)
+ for _ in range(num_padding):
+ frame = torch.hstack((frame, inputs[-1]))
+ LFR_inputs.append(frame)
+ LFR_outputs = torch.vstack(LFR_inputs)
+ return LFR_outputs.type(torch.float32)
+
+
+@tables.register("frontend_classes", "WavFrontend")
+class WavFrontend(nn.Module):
+ """Conventional frontend structure for ASR."""
+
+ def __init__(
+ self,
+ cmvn_file: str = None,
+ fs: int = 16000,
+ window: str = "hamming",
+ n_mels: int = 80,
+ frame_length: int = 25,
+ frame_shift: int = 10,
+ filter_length_min: int = -1,
+ filter_length_max: int = -1,
+ lfr_m: int = 1,
+ lfr_n: int = 1,
+ dither: float = 1.0,
+ snip_edges: bool = True,
+ upsacle_samples: bool = True,
+ **kwargs,
+ ):
+ super().__init__()
+ self.fs = fs
+ self.window = window
+ self.n_mels = n_mels
+ self.frame_length = frame_length
+ self.frame_shift = frame_shift
+ self.filter_length_min = filter_length_min
+ self.filter_length_max = filter_length_max
+ self.lfr_m = lfr_m
+ self.lfr_n = lfr_n
+ self.cmvn_file = cmvn_file
+ self.dither = dither
+ self.snip_edges = snip_edges
+ self.upsacle_samples = upsacle_samples
+ self.cmvn = None if self.cmvn_file is None else load_cmvn(self.cmvn_file)
+
+ def output_size(self) -> int:
+ return self.n_mels * self.lfr_m
+
+ def forward(
+ self,
+ input: torch.Tensor,
+ input_lengths,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ batch_size = input.size(0)
+ feats = []
+ feats_lens = []
+ for i in range(batch_size):
+ waveform_length = input_lengths[i]
+ waveform = input[i][:waveform_length]
+ if self.upsacle_samples:
+ waveform = waveform * (1 << 15)
+ waveform = waveform.unsqueeze(0)
+ mat = kaldi.fbank(
+ waveform,
+ num_mel_bins=self.n_mels,
+ frame_length=self.frame_length,
+ frame_shift=self.frame_shift,
+ dither=self.dither,
+ energy_floor=0.0,
+ window_type=self.window,
+ sample_frequency=self.fs,
+ snip_edges=self.snip_edges,
+ )
+
+ if self.lfr_m != 1 or self.lfr_n != 1:
+ mat = apply_lfr(mat, self.lfr_m, self.lfr_n)
+ if self.cmvn is not None:
+ mat = apply_cmvn(mat, self.cmvn)
+ feat_length = mat.size(0)
+ feats.append(mat)
+ feats_lens.append(feat_length)
+
+ feats_lens = torch.as_tensor(feats_lens)
+ if batch_size == 1:
+ feats_pad = feats[0][None, :, :]
+ else:
+ feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0)
+ return feats_pad, feats_lens
+
+ def forward_fbank(
+ self, input: torch.Tensor, input_lengths: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ batch_size = input.size(0)
+ feats = []
+ feats_lens = []
+ for i in range(batch_size):
+ waveform_length = input_lengths[i]
+ waveform = input[i][:waveform_length]
+ waveform = waveform * (1 << 15)
+ waveform = waveform.unsqueeze(0)
+ mat = kaldi.fbank(
+ waveform,
+ num_mel_bins=self.n_mels,
+ frame_length=self.frame_length,
+ frame_shift=self.frame_shift,
+ dither=self.dither,
+ energy_floor=0.0,
+ window_type=self.window,
+ sample_frequency=self.fs,
+ )
+
+ feat_length = mat.size(0)
+ feats.append(mat)
+ feats_lens.append(feat_length)
+
+ feats_lens = torch.as_tensor(feats_lens)
+ feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0)
+ return feats_pad, feats_lens
+
+ def forward_lfr_cmvn(
+ self, input: torch.Tensor, input_lengths: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ batch_size = input.size(0)
+ feats = []
+ feats_lens = []
+ for i in range(batch_size):
+ mat = input[i, : input_lengths[i], :]
+ if self.lfr_m != 1 or self.lfr_n != 1:
+ mat = apply_lfr(mat, self.lfr_m, self.lfr_n)
+ if self.cmvn is not None:
+ mat = apply_cmvn(mat, self.cmvn)
+ feat_length = mat.size(0)
+ feats.append(mat)
+ feats_lens.append(feat_length)
+
+ feats_lens = torch.as_tensor(feats_lens)
+ feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0)
+ return feats_pad, feats_lens
+
+
+@tables.register("frontend_classes", "WavFrontendOnline")
+class WavFrontendOnline(nn.Module):
+ """Conventional frontend structure for streaming ASR/VAD."""
+
+ def __init__(
+ self,
+ cmvn_file: str = None,
+ fs: int = 16000,
+ window: str = "hamming",
+ n_mels: int = 80,
+ frame_length: int = 25,
+ frame_shift: int = 10,
+ filter_length_min: int = -1,
+ filter_length_max: int = -1,
+ lfr_m: int = 1,
+ lfr_n: int = 1,
+ dither: float = 1.0,
+ snip_edges: bool = True,
+ upsacle_samples: bool = True,
+ **kwargs,
+ ):
+ super().__init__()
+ self.fs = fs
+ self.window = window
+ self.n_mels = n_mels
+ self.frame_length = frame_length
+ self.frame_shift = frame_shift
+ self.frame_sample_length = int(self.frame_length * self.fs / 1000)
+ self.frame_shift_sample_length = int(self.frame_shift * self.fs / 1000)
+ self.filter_length_min = filter_length_min
+ self.filter_length_max = filter_length_max
+ self.lfr_m = lfr_m
+ self.lfr_n = lfr_n
+ self.cmvn_file = cmvn_file
+ self.dither = dither
+ self.snip_edges = snip_edges
+ self.upsacle_samples = upsacle_samples
+ # self.waveforms = None
+ # self.reserve_waveforms = None
+ # self.fbanks = None
+ # self.fbanks_lens = None
+ self.cmvn = None if self.cmvn_file is None else load_cmvn(self.cmvn_file)
+ # self.input_cache = None
+ # self.lfr_splice_cache = []
+
+ def output_size(self) -> int:
+ return self.n_mels * self.lfr_m
+
+ @staticmethod
+ def apply_cmvn(inputs: torch.Tensor, cmvn: torch.Tensor) -> torch.Tensor:
+ """
+ Apply CMVN with mvn data
+ """
+
+ device = inputs.device
+ dtype = inputs.dtype
+ frame, dim = inputs.shape
+
+ means = np.tile(cmvn[0:1, :dim], (frame, 1))
+ vars = np.tile(cmvn[1:2, :dim], (frame, 1))
+ inputs += torch.from_numpy(means).type(dtype).to(device)
+ inputs *= torch.from_numpy(vars).type(dtype).to(device)
+
+ return inputs.type(torch.float32)
+
+ @staticmethod
+ def apply_lfr(
+ inputs: torch.Tensor, lfr_m: int, lfr_n: int, is_final: bool = False
+ ) -> Tuple[torch.Tensor, torch.Tensor, int]:
+ """
+ Apply lfr with data
+ """
+
+ LFR_inputs = []
+ # inputs = torch.vstack((inputs_lfr_cache, inputs))
+ T = inputs.shape[0] # include the right context
+ T_lfr = int(
+ np.ceil((T - (lfr_m - 1) // 2) / lfr_n)
+ ) # minus the right context: (lfr_m - 1) // 2
+ splice_idx = T_lfr
+ for i in range(T_lfr):
+ if lfr_m <= T - i * lfr_n:
+ LFR_inputs.append((inputs[i * lfr_n : i * lfr_n + lfr_m]).view(1, -1))
+ else: # process last LFR frame
+ if is_final:
+ num_padding = lfr_m - (T - i * lfr_n)
+ frame = (inputs[i * lfr_n :]).view(-1)
+ for _ in range(num_padding):
+ frame = torch.hstack((frame, inputs[-1]))
+ LFR_inputs.append(frame)
+ else:
+ # update splice_idx and break the circle
+ splice_idx = i
+ break
+ splice_idx = min(T - 1, splice_idx * lfr_n)
+ lfr_splice_cache = inputs[splice_idx:, :]
+ LFR_outputs = torch.vstack(LFR_inputs)
+ return LFR_outputs.type(torch.float32), lfr_splice_cache, splice_idx
+
+ @staticmethod
+ def compute_frame_num(
+ sample_length: int, frame_sample_length: int, frame_shift_sample_length: int
+ ) -> int:
+ frame_num = int(
+ (sample_length - frame_sample_length) / frame_shift_sample_length + 1
+ )
+ return (
+ frame_num if frame_num >= 1 and sample_length >= frame_sample_length else 0
+ )
+
+ def forward_fbank(
+ self,
+ input: torch.Tensor,
+ input_lengths: torch.Tensor,
+ cache: dict = {},
+ **kwargs,
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ batch_size = input.size(0)
+ assert batch_size == 1
+ input = torch.cat((cache["input_cache"], input), dim=1)
+ frame_num = self.compute_frame_num(
+ input.shape[-1], self.frame_sample_length, self.frame_shift_sample_length
+ )
+ # update self.in_cache
+ cache["input_cache"] = input[
+ :, -(input.shape[-1] - frame_num * self.frame_shift_sample_length) :
+ ]
+ waveforms = torch.empty(0)
+ feats_pad = torch.empty(0)
+ feats_lens = torch.empty(0)
+ if frame_num:
+ waveforms = []
+ feats = []
+ feats_lens = []
+ for i in range(batch_size):
+ waveform = input[i].cuda()
+ # we need accurate wave samples that used for fbank extracting
+ waveforms.append(
+ waveform[
+ : (
+ (frame_num - 1) * self.frame_shift_sample_length
+ + self.frame_sample_length
+ )
+ ]
+ )
+ waveform = waveform * (1 << 15)
+ waveform = waveform.unsqueeze(0)
+ mat = kaldi.fbank(
+ waveform,
+ num_mel_bins=self.n_mels,
+ frame_length=self.frame_length,
+ frame_shift=self.frame_shift,
+ dither=self.dither,
+ energy_floor=0.0,
+ window_type=self.window,
+ sample_frequency=self.fs,
+ )
+
+ feat_length = mat.size(0)
+ feats.append(mat)
+ feats_lens.append(feat_length)
+
+ waveforms = torch.stack(waveforms)
+ feats_lens = torch.as_tensor(feats_lens)
+ feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0)
+ cache["fbanks"] = feats_pad
+ cache["fbanks_lens"] = copy.deepcopy(feats_lens)
+ return waveforms, feats_pad, feats_lens
+
+ def forward_lfr_cmvn(
+ self,
+ input: torch.Tensor,
+ input_lengths: torch.Tensor,
+ is_final: bool = False,
+ cache: dict = {},
+ **kwargs,
+ ):
+ batch_size = input.size(0)
+ feats = []
+ feats_lens = []
+ lfr_splice_frame_idxs = []
+ for i in range(batch_size):
+ mat = input[i, : input_lengths[i], :]
+ if self.lfr_m != 1 or self.lfr_n != 1:
+ # update self.lfr_splice_cache in self.apply_lfr
+ # mat, self.lfr_splice_cache[i], lfr_splice_frame_idx = self.apply_lfr(mat, self.lfr_m, self.lfr_n, self.lfr_splice_cache[i],
+ mat, cache["lfr_splice_cache"][i], lfr_splice_frame_idx = (
+ self.apply_lfr(mat, self.lfr_m, self.lfr_n, is_final)
+ )
+ if self.cmvn_file is not None:
+ mat = self.apply_cmvn(mat, self.cmvn)
+ feat_length = mat.size(0)
+ feats.append(mat)
+ feats_lens.append(feat_length)
+ lfr_splice_frame_idxs.append(lfr_splice_frame_idx)
+ feats_lens = torch.as_tensor(feats_lens)
+ feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0)
+ lfr_splice_frame_idxs = torch.as_tensor(lfr_splice_frame_idxs)
+ return feats_pad, feats_lens, lfr_splice_frame_idxs
+
+ def forward(self, input: torch.Tensor, input_lengths: torch.Tensor, **kwargs):
+ is_final = kwargs.get("is_final", False)
+ cache = kwargs.get("cache", {})
+ if len(cache) == 0:
+ self.init_cache(cache)
+
+ batch_size = input.shape[0]
+ assert (
+ batch_size == 1
+ ), "we support to extract feature online only when the batch size is equal to 1 now"
+
+ waveforms, feats, feats_lengths = self.forward_fbank(
+ input, input_lengths, cache=cache
+ ) # input shape: B T D
+
+ if feats.shape[0]:
+
+ cache["waveforms"] = torch.cat(
+ (cache["reserve_waveforms"], waveforms.cpu()), dim=1
+ )
+
+ if not cache["lfr_splice_cache"]: # 初始化splice_cache
+ for i in range(batch_size):
+ cache["lfr_splice_cache"].append(
+ feats[i][0, :].unsqueeze(dim=0).repeat((self.lfr_m - 1) // 2, 1)
+ )
+ # need the number of the input frames + self.lfr_splice_cache[0].shape[0] is greater than self.lfr_m
+ if feats_lengths[0] + cache["lfr_splice_cache"][0].shape[0] >= self.lfr_m:
+ lfr_splice_cache_tensor = torch.stack(
+ cache["lfr_splice_cache"]
+ ) # B T D
+ feats = torch.cat((lfr_splice_cache_tensor, feats), dim=1)
+
+ feats_lengths += lfr_splice_cache_tensor[0].shape[0]
+ frame_from_waveforms = int(
+ (cache["waveforms"].shape[1] - self.frame_sample_length)
+ / self.frame_shift_sample_length
+ + 1
+ )
+ minus_frame = (
+ (self.lfr_m - 1) // 2
+ if cache["reserve_waveforms"].numel() == 0
+ else 0
+ )
+ feats, feats_lengths, lfr_splice_frame_idxs = self.forward_lfr_cmvn(
+ feats, feats_lengths, is_final, cache=cache
+ )
+ if self.lfr_m == 1:
+ cache["reserve_waveforms"] = torch.empty(0)
+ else:
+ reserve_frame_idx = lfr_splice_frame_idxs[0] - minus_frame
+ # print('reserve_frame_idx: ' + str(reserve_frame_idx))
+ # print('frame_frame: ' + str(frame_from_waveforms))
+ cache["reserve_waveforms"] = cache["waveforms"][
+ :,
+ reserve_frame_idx
+ * self.frame_shift_sample_length : frame_from_waveforms
+ * self.frame_shift_sample_length,
+ ]
+ sample_length = (
+ frame_from_waveforms - 1
+ ) * self.frame_shift_sample_length + self.frame_sample_length
+ cache["waveforms"] = cache["waveforms"][:, :sample_length]
+ else:
+ # update self.reserve_waveforms and self.lfr_splice_cache
+ cache["reserve_waveforms"] = cache["waveforms"][
+ :, : -(self.frame_sample_length - self.frame_shift_sample_length)
+ ]
+ for i in range(batch_size):
+ cache["lfr_splice_cache"][i] = torch.cat(
+ (cache["lfr_splice_cache"][i], feats[i]), dim=0
+ )
+ return torch.empty(0), feats_lengths
+ else:
+ if is_final:
+ cache["waveforms"] = (
+ waveforms
+ if cache["reserve_waveforms"].numel() == 0
+ else cache["reserve_waveforms"]
+ )
+ feats = torch.stack(cache["lfr_splice_cache"])
+ feats_lengths = (
+ torch.zeros(batch_size, dtype=torch.int) + feats.shape[1]
+ )
+ feats, feats_lengths, _ = self.forward_lfr_cmvn(
+ feats, feats_lengths, is_final, cache=cache
+ )
+ # if is_final:
+ # self.init_cache(cache)
+ return feats, feats_lengths
+
+ def init_cache(self, cache: dict = {}):
+ cache["reserve_waveforms"] = torch.empty(0)
+ cache["input_cache"] = torch.empty(0)
+ cache["lfr_splice_cache"] = []
+ cache["waveforms"] = None
+ cache["fbanks"] = None
+ cache["fbanks_lens"] = None
+ return cache
+
+
+class WavFrontendMel23(nn.Module):
+ """Conventional frontend structure for ASR."""
+
+ def __init__(
+ self,
+ fs: int = 16000,
+ frame_length: int = 25,
+ frame_shift: int = 10,
+ lfr_m: int = 1,
+ lfr_n: int = 1,
+ **kwargs,
+ ):
+ super().__init__()
+ self.fs = fs
+ self.frame_length = frame_length
+ self.frame_shift = frame_shift
+ self.lfr_m = lfr_m
+ self.lfr_n = lfr_n
+ self.n_mels = 23
+
+ def output_size(self) -> int:
+ return self.n_mels * (2 * self.lfr_m + 1)
+
+ def forward(
+ self, input: torch.Tensor, input_lengths: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ batch_size = input.size(0)
+ feats = []
+ feats_lens = []
+ for i in range(batch_size):
+ waveform_length = input_lengths[i]
+ waveform = input[i][:waveform_length]
+ waveform = waveform.numpy()
+ mat = eend_ola_feature.stft(waveform, self.frame_length, self.frame_shift)
+ mat = eend_ola_feature.transform(mat)
+ mat = eend_ola_feature.splice(mat, context_size=self.lfr_m)
+ mat = mat[:: self.lfr_n]
+ mat = torch.from_numpy(mat)
+ feat_length = mat.size(0)
+ feats.append(mat)
+ feats_lens.append(feat_length)
+
+ feats_lens = torch.as_tensor(feats_lens)
+ feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0)
+ return feats_pad, feats_lens
diff --git a/almeval/models/stepaudio/funasr_detach/frontends/windowing.py b/almeval/models/stepaudio/funasr_detach/frontends/windowing.py
new file mode 100644
index 0000000000000000000000000000000000000000..84ec8e200e2cbae7e6c4f50e33e3ba933b03195f
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/frontends/windowing.py
@@ -0,0 +1,74 @@
+#!/usr/bin/env python3
+# 2020, Technische Universität München; Ludwig Kürzinger
+# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+"""Sliding Window for raw audio input data."""
+
+import torch
+import torch.nn as nn
+from typing import Tuple
+
+
+class SlidingWindow(nn.Module):
+ """Sliding Window.
+ Provides a sliding window over a batched continuous raw audio tensor.
+ Optionally, provides padding (Currently not implemented).
+ Combine this module with a pre-encoder compatible with raw audio data,
+ for example Sinc convolutions.
+ Known issues:
+ Output length is calculated incorrectly if audio shorter than win_length.
+ WARNING: trailing values are discarded - padding not implemented yet.
+ There is currently no additional window function applied to input values.
+ """
+
+ def __init__(
+ self,
+ win_length: int = 400,
+ hop_length: int = 160,
+ channels: int = 1,
+ padding: int = None,
+ fs=None,
+ ):
+ """Initialize.
+ Args:
+ win_length: Length of frame.
+ hop_length: Relative starting point of next frame.
+ channels: Number of input channels.
+ padding: Padding (placeholder, currently not implemented).
+ fs: Sampling rate (placeholder for compatibility, not used).
+ """
+ super().__init__()
+ self.fs = fs
+ self.win_length = win_length
+ self.hop_length = hop_length
+ self.channels = channels
+ self.padding = padding
+
+ def forward(
+ self, input: torch.Tensor, input_lengths: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Apply a sliding window on the input.
+ Args:
+ input: Input (B, T, C*D) or (B, T*C*D), with D=C=1.
+ input_lengths: Input lengths within batch.
+ Returns:
+ Tensor: Output with dimensions (B, T, C, D), with D=win_length.
+ Tensor: Output lengths within batch.
+ """
+ input_size = input.size()
+ B = input_size[0]
+ T = input_size[1]
+ C = self.channels
+ D = self.win_length
+ # (B, T, C) --> (T, B, C)
+ continuous = input.view(B, T, C).permute(1, 0, 2)
+ windowed = continuous.unfold(0, D, self.hop_length)
+ # (T, B, C, D) --> (B, T, C, D)
+ output = windowed.permute(1, 0, 2, 3).contiguous()
+ # After unfold(), windowed lengths change:
+ output_lengths = (input_lengths - self.win_length) // self.hop_length + 1
+ return output, output_lengths
+
+ def output_size(self) -> int:
+ """Return output length of feature dimension D, i.e. the window length."""
+ return self.win_length
diff --git a/almeval/models/stepaudio/funasr_detach/metrics/__init__.py b/almeval/models/stepaudio/funasr_detach/metrics/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/almeval/models/stepaudio/funasr_detach/metrics/common.py b/almeval/models/stepaudio/funasr_detach/metrics/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..92f90796a3a230b3bfc47ebe8d9292fae37a1b9c
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/metrics/common.py
@@ -0,0 +1,249 @@
+#!/usr/bin/env python3
+# encoding: utf-8
+
+# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
+# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+"""Common functions for ASR."""
+
+import json
+import logging
+import sys
+
+from itertools import groupby
+import numpy as np
+import six
+
+
+def end_detect(ended_hyps, i, M=3, D_end=np.log(1 * np.exp(-10))):
+ """End detection.
+
+ described in Eq. (50) of S. Watanabe et al
+ "Hybrid CTC/Attention Architecture for End-to-End Speech Recognition"
+
+ :param ended_hyps:
+ :param i:
+ :param M:
+ :param D_end:
+ :return:
+ """
+ if len(ended_hyps) == 0:
+ return False
+ count = 0
+ best_hyp = sorted(ended_hyps, key=lambda x: x["score"], reverse=True)[0]
+ for m in six.moves.range(M):
+ # get ended_hyps with their length is i - m
+ hyp_length = i - m
+ hyps_same_length = [x for x in ended_hyps if len(x["yseq"]) == hyp_length]
+ if len(hyps_same_length) > 0:
+ best_hyp_same_length = sorted(
+ hyps_same_length, key=lambda x: x["score"], reverse=True
+ )[0]
+ if best_hyp_same_length["score"] - best_hyp["score"] < D_end:
+ count += 1
+
+ if count == M:
+ return True
+ else:
+ return False
+
+
+# TODO(takaaki-hori): add different smoothing methods
+def label_smoothing_dist(odim, lsm_type, transcript=None, blank=0):
+ """Obtain label distribution for loss smoothing.
+
+ :param odim:
+ :param lsm_type:
+ :param blank:
+ :param transcript:
+ :return:
+ """
+ if transcript is not None:
+ with open(transcript, "rb") as f:
+ trans_json = json.load(f)["utts"]
+
+ if lsm_type == "unigram":
+ assert transcript is not None, (
+ "transcript is required for %s label smoothing" % lsm_type
+ )
+ labelcount = np.zeros(odim)
+ for k, v in trans_json.items():
+ ids = np.array([int(n) for n in v["output"][0]["tokenid"].split()])
+ # to avoid an error when there is no text in an uttrance
+ if len(ids) > 0:
+ labelcount[ids] += 1
+ labelcount[odim - 1] = len(transcript) # count
+ labelcount[labelcount == 0] = 1 # flooring
+ labelcount[blank] = 0 # remove counts for blank
+ labeldist = labelcount.astype(np.float32) / np.sum(labelcount)
+ else:
+ logging.error("Error: unexpected label smoothing type: %s" % lsm_type)
+ sys.exit()
+
+ return labeldist
+
+
+def get_vgg2l_odim(idim, in_channel=3, out_channel=128):
+ """Return the output size of the VGG frontend.
+
+ :param in_channel: input channel size
+ :param out_channel: output channel size
+ :return: output size
+ :rtype int
+ """
+ idim = idim / in_channel
+ idim = np.ceil(np.array(idim, dtype=np.float32) / 2) # 1st max pooling
+ idim = np.ceil(np.array(idim, dtype=np.float32) / 2) # 2nd max pooling
+ return int(idim) * out_channel # numer of channels
+
+
+class ErrorCalculator(object):
+ """Calculate CER and WER for E2E_ASR and CTC models during training.
+
+ :param y_hats: numpy array with predicted text
+ :param y_pads: numpy array with true (target) text
+ :param char_list:
+ :param sym_space:
+ :param sym_blank:
+ :return:
+ """
+
+ def __init__(
+ self, char_list, sym_space, sym_blank, report_cer=False, report_wer=False
+ ):
+ """Construct an ErrorCalculator object."""
+ super(ErrorCalculator, self).__init__()
+
+ self.report_cer = report_cer
+ self.report_wer = report_wer
+
+ self.char_list = char_list
+ self.space = sym_space
+ self.blank = sym_blank
+ self.idx_blank = self.char_list.index(self.blank)
+ if self.space in self.char_list:
+ self.idx_space = self.char_list.index(self.space)
+ else:
+ self.idx_space = None
+
+ def __call__(self, ys_hat, ys_pad, is_ctc=False):
+ """Calculate sentence-level WER/CER score.
+
+ :param torch.Tensor ys_hat: prediction (batch, seqlen)
+ :param torch.Tensor ys_pad: reference (batch, seqlen)
+ :param bool is_ctc: calculate CER score for CTC
+ :return: sentence-level WER score
+ :rtype float
+ :return: sentence-level CER score
+ :rtype float
+ """
+ cer, wer = None, None
+ if is_ctc:
+ return self.calculate_cer_ctc(ys_hat, ys_pad)
+ elif not self.report_cer and not self.report_wer:
+ return cer, wer
+
+ seqs_hat, seqs_true = self.convert_to_char(ys_hat, ys_pad)
+ if self.report_cer:
+ cer = self.calculate_cer(seqs_hat, seqs_true)
+
+ if self.report_wer:
+ wer = self.calculate_wer(seqs_hat, seqs_true)
+ return cer, wer
+
+ def calculate_cer_ctc(self, ys_hat, ys_pad):
+ """Calculate sentence-level CER score for CTC.
+
+ :param torch.Tensor ys_hat: prediction (batch, seqlen)
+ :param torch.Tensor ys_pad: reference (batch, seqlen)
+ :return: average sentence-level CER score
+ :rtype float
+ """
+ import editdistance
+
+ cers, char_ref_lens = [], []
+ for i, y in enumerate(ys_hat):
+ y_hat = [x[0] for x in groupby(y)]
+ y_true = ys_pad[i]
+ seq_hat, seq_true = [], []
+ for idx in y_hat:
+ idx = int(idx)
+ if idx != -1 and idx != self.idx_blank and idx != self.idx_space:
+ seq_hat.append(self.char_list[int(idx)])
+
+ for idx in y_true:
+ idx = int(idx)
+ if idx != -1 and idx != self.idx_blank and idx != self.idx_space:
+ seq_true.append(self.char_list[int(idx)])
+
+ hyp_chars = "".join(seq_hat)
+ ref_chars = "".join(seq_true)
+ if len(ref_chars) > 0:
+ cers.append(editdistance.eval(hyp_chars, ref_chars))
+ char_ref_lens.append(len(ref_chars))
+
+ cer_ctc = float(sum(cers)) / sum(char_ref_lens) if cers else None
+ return cer_ctc
+
+ def convert_to_char(self, ys_hat, ys_pad):
+ """Convert index to character.
+
+ :param torch.Tensor seqs_hat: prediction (batch, seqlen)
+ :param torch.Tensor seqs_true: reference (batch, seqlen)
+ :return: token list of prediction
+ :rtype list
+ :return: token list of reference
+ :rtype list
+ """
+ seqs_hat, seqs_true = [], []
+ for i, y_hat in enumerate(ys_hat):
+ y_true = ys_pad[i]
+ eos_true = np.where(y_true == -1)[0]
+ ymax = eos_true[0] if len(eos_true) > 0 else len(y_true)
+ # NOTE: padding index (-1) in y_true is used to pad y_hat
+ seq_hat = [self.char_list[int(idx)] for idx in y_hat[:ymax]]
+ seq_true = [self.char_list[int(idx)] for idx in y_true if int(idx) != -1]
+ seq_hat_text = "".join(seq_hat).replace(self.space, " ")
+ seq_hat_text = seq_hat_text.replace(self.blank, "")
+ seq_true_text = "".join(seq_true).replace(self.space, " ")
+ seqs_hat.append(seq_hat_text)
+ seqs_true.append(seq_true_text)
+ return seqs_hat, seqs_true
+
+ def calculate_cer(self, seqs_hat, seqs_true):
+ """Calculate sentence-level CER score.
+
+ :param list seqs_hat: prediction
+ :param list seqs_true: reference
+ :return: average sentence-level CER score
+ :rtype float
+ """
+ import editdistance
+
+ char_eds, char_ref_lens = [], []
+ for i, seq_hat_text in enumerate(seqs_hat):
+ seq_true_text = seqs_true[i]
+ hyp_chars = seq_hat_text.replace(" ", "")
+ ref_chars = seq_true_text.replace(" ", "")
+ char_eds.append(editdistance.eval(hyp_chars, ref_chars))
+ char_ref_lens.append(len(ref_chars))
+ return float(sum(char_eds)) / sum(char_ref_lens)
+
+ def calculate_wer(self, seqs_hat, seqs_true):
+ """Calculate sentence-level WER score.
+
+ :param list seqs_hat: prediction
+ :param list seqs_true: reference
+ :return: average sentence-level WER score
+ :rtype float
+ """
+ import editdistance
+
+ word_eds, word_ref_lens = [], []
+ for i, seq_hat_text in enumerate(seqs_hat):
+ seq_true_text = seqs_true[i]
+ hyp_words = seq_hat_text.split()
+ ref_words = seq_true_text.split()
+ word_eds.append(editdistance.eval(hyp_words, ref_words))
+ word_ref_lens.append(len(ref_words))
+ return float(sum(word_eds)) / sum(word_ref_lens)
diff --git a/almeval/models/stepaudio/funasr_detach/metrics/compute_acc.py b/almeval/models/stepaudio/funasr_detach/metrics/compute_acc.py
new file mode 100644
index 0000000000000000000000000000000000000000..25c3591203d1358e9d56eadf19d6cce23d8739c0
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/metrics/compute_acc.py
@@ -0,0 +1,24 @@
+import torch
+
+
+def th_accuracy(pad_outputs, pad_targets, ignore_label):
+ """Calculate accuracy.
+
+ Args:
+ pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
+ pad_targets (LongTensor): Target label tensors (B, Lmax, D).
+ ignore_label (int): Ignore label id.
+
+ Returns:
+ float: Accuracy value (0.0 - 1.0).
+
+ """
+ pad_pred = pad_outputs.view(
+ pad_targets.size(0), pad_targets.size(1), pad_outputs.size(1)
+ ).argmax(2)
+ mask = pad_targets != ignore_label
+ numerator = torch.sum(
+ pad_pred.masked_select(mask) == pad_targets.masked_select(mask)
+ )
+ denominator = torch.sum(mask)
+ return float(numerator) / float(denominator)
diff --git a/almeval/models/stepaudio/funasr_detach/metrics/compute_eer.py b/almeval/models/stepaudio/funasr_detach/metrics/compute_eer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5180b16694f629c532e13c94912f7e056dd8f0bf
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/metrics/compute_eer.py
@@ -0,0 +1,59 @@
+import numpy as np
+from sklearn.metrics import roc_curve
+import argparse
+
+
+def _compute_eer(label, pred, positive_label=1):
+ """
+ Python compute equal error rate (eer)
+ ONLY tested on binary classification
+
+ :param label: ground-truth label, should be a 1-d list or np.array, each element represents the ground-truth label of one sample
+ :param pred: model prediction, should be a 1-d list or np.array, each element represents the model prediction of one sample
+ :param positive_label: the class that is viewed as positive class when computing EER
+ :return: equal error rate (EER)
+ """
+
+ # all fpr, tpr, fnr, fnr, threshold are lists (in the format of np.array)
+ fpr, tpr, threshold = roc_curve(label, pred, pos_label=positive_label)
+ fnr = 1 - tpr
+
+ # the threshold of fnr == fpr
+ eer_threshold = threshold[np.nanargmin(np.absolute((fnr - fpr)))]
+
+ # theoretically eer from fpr and eer from fnr should be identical but they can be slightly differ in reality
+ eer_1 = fpr[np.nanargmin(np.absolute((fnr - fpr)))]
+ eer_2 = fnr[np.nanargmin(np.absolute((fnr - fpr)))]
+
+ # return the mean of eer from fpr and from fnr
+ eer = (eer_1 + eer_2) / 2
+ return eer, eer_threshold
+
+
+def compute_eer(trials_path, scores_path):
+ labels = []
+ for one_line in open(trials_path, "r"):
+ labels.append(one_line.strip().rsplit(" ", 1)[-1] == "target")
+ labels = np.array(labels, dtype=int)
+
+ scores = []
+ for one_line in open(scores_path, "r"):
+ scores.append(float(one_line.strip().rsplit(" ", 1)[-1]))
+ scores = np.array(scores, dtype=float)
+
+ eer, threshold = _compute_eer(labels, scores)
+ return eer, threshold
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("trials", help="trial list")
+ parser.add_argument("scores", help="score file, normalized to [0, 1]")
+ args = parser.parse_args()
+
+ eer, threshold = compute_eer(args.trials, args.scores)
+ print("EER is {:.4f} at threshold {:.4f}".format(eer * 100.0, threshold))
+
+
+if __name__ == "__main__":
+ main()
diff --git a/almeval/models/stepaudio/funasr_detach/metrics/compute_min_dcf.py b/almeval/models/stepaudio/funasr_detach/metrics/compute_min_dcf.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3da8d40a12f6b2a5fd7933083262769a218d6fe
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/metrics/compute_min_dcf.py
@@ -0,0 +1,186 @@
+#!/usr/bin/env python3
+# Copyright 2018 David Snyder
+# Apache 2.0
+
+# This script computes the minimum detection cost function, which is a common
+# error metric used in speaker recognition. Compared to equal error-rate,
+# which assigns equal weight to false negatives and false positives, this
+# error-rate is usually used to assess performance in settings where achieving
+# a low false positive rate is more important than achieving a low false
+# negative rate. See the NIST 2016 Speaker Recognition Evaluation Plan at
+# https://www.nist.gov/sites/default/files/documents/2016/10/07/sre16_eval_plan_v1.3.pdf
+# for more details about the metric.
+from __future__ import print_function
+from operator import itemgetter
+import sys, argparse, os
+
+
+def GetArgs():
+ parser = argparse.ArgumentParser(
+ description="Compute the minimum "
+ "detection cost function along with the threshold at which it occurs. "
+ "Usage: sid/compute_min_dcf.py [options...] "
+ " "
+ "E.g., sid/compute_min_dcf.py --p-target 0.01 --c-miss 1 --c-fa 1 "
+ "exp/scores/trials data/test/trials",
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+ )
+ parser.add_argument(
+ "--p-target",
+ type=float,
+ dest="p_target",
+ default=0.01,
+ help="The prior probability of the target speaker in a trial.",
+ )
+ parser.add_argument(
+ "--c-miss",
+ type=float,
+ dest="c_miss",
+ default=1,
+ help="Cost of a missed detection. This is usually not changed.",
+ )
+ parser.add_argument(
+ "--c-fa",
+ type=float,
+ dest="c_fa",
+ default=1,
+ help="Cost of a spurious detection. This is usually not changed.",
+ )
+ parser.add_argument(
+ "scores_filename",
+ help="Input scores file, with columns of the form " " ",
+ )
+ parser.add_argument(
+ "trials_filename",
+ help="Input trials file, with columns of the form "
+ " ",
+ )
+ sys.stderr.write(" ".join(sys.argv) + "\n")
+ args = parser.parse_args()
+ args = CheckArgs(args)
+ return args
+
+
+def CheckArgs(args):
+ if args.c_fa <= 0:
+ raise Exception("--c-fa must be greater than 0")
+ if args.c_miss <= 0:
+ raise Exception("--c-miss must be greater than 0")
+ if args.p_target <= 0 or args.p_target >= 1:
+ raise Exception("--p-target must be greater than 0 and less than 1")
+ return args
+
+
+# Creates a list of false-negative rates, a list of false-positive rates
+# and a list of decision thresholds that give those error-rates.
+def ComputeErrorRates(scores, labels):
+
+ # Sort the scores from smallest to largest, and also get the corresponding
+ # indexes of the sorted scores. We will treat the sorted scores as the
+ # thresholds at which the the error-rates are evaluated.
+ sorted_indexes, thresholds = zip(
+ *sorted(
+ [(index, threshold) for index, threshold in enumerate(scores)],
+ key=itemgetter(1),
+ )
+ )
+ labels = [labels[i] for i in sorted_indexes]
+ fns = []
+ tns = []
+
+ # At the end of this loop, fns[i] is the number of errors made by
+ # incorrectly rejecting scores less than thresholds[i]. And, tns[i]
+ # is the total number of times that we have correctly rejected scores
+ # less than thresholds[i].
+ for i in range(0, len(labels)):
+ if i == 0:
+ fns.append(labels[i])
+ tns.append(1 - labels[i])
+ else:
+ fns.append(fns[i - 1] + labels[i])
+ tns.append(tns[i - 1] + 1 - labels[i])
+ positives = sum(labels)
+ negatives = len(labels) - positives
+
+ # Now divide the false negatives by the total number of
+ # positives to obtain the false negative rates across
+ # all thresholds
+ fnrs = [fn / float(positives) for fn in fns]
+
+ # Divide the true negatives by the total number of
+ # negatives to get the true negative rate. Subtract these
+ # quantities from 1 to get the false positive rates.
+ fprs = [1 - tn / float(negatives) for tn in tns]
+ return fnrs, fprs, thresholds
+
+
+# Computes the minimum of the detection cost function. The comments refer to
+# equations in Section 3 of the NIST 2016 Speaker Recognition Evaluation Plan.
+def ComputeMinDcf(fnrs, fprs, thresholds, p_target, c_miss, c_fa):
+ min_c_det = float("inf")
+ min_c_det_threshold = thresholds[0]
+ for i in range(0, len(fnrs)):
+ # See Equation (2). it is a weighted sum of false negative
+ # and false positive errors.
+ c_det = c_miss * fnrs[i] * p_target + c_fa * fprs[i] * (1 - p_target)
+ if c_det < min_c_det:
+ min_c_det = c_det
+ min_c_det_threshold = thresholds[i]
+ # See Equations (3) and (4). Now we normalize the cost.
+ c_def = min(c_miss * p_target, c_fa * (1 - p_target))
+ min_dcf = min_c_det / c_def
+ return min_dcf, min_c_det_threshold
+
+
+def compute_min_dcf(scores_filename, trials_filename, c_miss=1, c_fa=1, p_target=0.01):
+ scores_file = open(scores_filename, "r").readlines()
+ trials_file = open(trials_filename, "r").readlines()
+ c_miss = c_miss
+ c_fa = c_fa
+ p_target = p_target
+
+ scores = []
+ labels = []
+
+ trials = {}
+ for line in trials_file:
+ utt1, utt2, target = line.rstrip().split()
+ trial = utt1 + " " + utt2
+ trials[trial] = target
+
+ for line in scores_file:
+ utt1, utt2, score = line.rstrip().split()
+ trial = utt1 + " " + utt2
+ if trial in trials:
+ scores.append(float(score))
+ if trials[trial] == "target":
+ labels.append(1)
+ else:
+ labels.append(0)
+ else:
+ raise Exception(
+ "Missing entry for " + utt1 + " and " + utt2 + " " + scores_filename
+ )
+
+ fnrs, fprs, thresholds = ComputeErrorRates(scores, labels)
+ mindcf, threshold = ComputeMinDcf(fnrs, fprs, thresholds, p_target, c_miss, c_fa)
+ return mindcf, threshold
+
+
+def main():
+ args = GetArgs()
+ mindcf, threshold = compute_min_dcf(
+ args.scores_filename,
+ args.trials_filename,
+ args.c_miss,
+ args.c_fa,
+ args.p_target,
+ )
+ sys.stdout.write(
+ "minDCF is {0:.4f} at threshold {1:.4f} (p-target={2}, c-miss={3}, "
+ "c-fa={4})\n".format(mindcf, threshold, args.p_target, args.c_miss, args.c_fa)
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/almeval/models/stepaudio/funasr_detach/metrics/compute_wer.py b/almeval/models/stepaudio/funasr_detach/metrics/compute_wer.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f86be25faa54bde1e5da12483fbe00fc3ff8827
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/metrics/compute_wer.py
@@ -0,0 +1,203 @@
+import os
+import numpy as np
+import sys
+
+
+def compute_wer(ref_file, hyp_file, cer_detail_file):
+ rst = {
+ "Wrd": 0,
+ "Corr": 0,
+ "Ins": 0,
+ "Del": 0,
+ "Sub": 0,
+ "Snt": 0,
+ "Err": 0.0,
+ "S.Err": 0.0,
+ "wrong_words": 0,
+ "wrong_sentences": 0,
+ }
+
+ hyp_dict = {}
+ ref_dict = {}
+ with open(hyp_file, "r") as hyp_reader:
+ for line in hyp_reader:
+ key = line.strip().split()[0]
+ value = line.strip().split()[1:]
+ hyp_dict[key] = value
+ with open(ref_file, "r") as ref_reader:
+ for line in ref_reader:
+ key = line.strip().split()[0]
+ value = line.strip().split()[1:]
+ ref_dict[key] = value
+
+ cer_detail_writer = open(cer_detail_file, "w")
+ for hyp_key in hyp_dict:
+ if hyp_key in ref_dict:
+ out_item = compute_wer_by_line(hyp_dict[hyp_key], ref_dict[hyp_key])
+ rst["Wrd"] += out_item["nwords"]
+ rst["Corr"] += out_item["cor"]
+ rst["wrong_words"] += out_item["wrong"]
+ rst["Ins"] += out_item["ins"]
+ rst["Del"] += out_item["del"]
+ rst["Sub"] += out_item["sub"]
+ rst["Snt"] += 1
+ if out_item["wrong"] > 0:
+ rst["wrong_sentences"] += 1
+ cer_detail_writer.write(hyp_key + print_cer_detail(out_item) + "\n")
+ cer_detail_writer.write(
+ "ref:"
+ + "\t"
+ + " ".join(list(map(lambda x: x.lower(), ref_dict[hyp_key])))
+ + "\n"
+ )
+ cer_detail_writer.write(
+ "hyp:"
+ + "\t"
+ + " ".join(list(map(lambda x: x.lower(), hyp_dict[hyp_key])))
+ + "\n"
+ )
+
+ if rst["Wrd"] > 0:
+ rst["Err"] = round(rst["wrong_words"] * 100 / rst["Wrd"], 2)
+ if rst["Snt"] > 0:
+ rst["S.Err"] = round(rst["wrong_sentences"] * 100 / rst["Snt"], 2)
+
+ cer_detail_writer.write("\n")
+ cer_detail_writer.write(
+ "%WER "
+ + str(rst["Err"])
+ + " [ "
+ + str(rst["wrong_words"])
+ + " / "
+ + str(rst["Wrd"])
+ + ", "
+ + str(rst["Ins"])
+ + " ins, "
+ + str(rst["Del"])
+ + " del, "
+ + str(rst["Sub"])
+ + " sub ]"
+ + "\n"
+ )
+ cer_detail_writer.write(
+ "%SER "
+ + str(rst["S.Err"])
+ + " [ "
+ + str(rst["wrong_sentences"])
+ + " / "
+ + str(rst["Snt"])
+ + " ]"
+ + "\n"
+ )
+ cer_detail_writer.write(
+ "Scored "
+ + str(len(hyp_dict))
+ + " sentences, "
+ + str(len(hyp_dict) - rst["Snt"])
+ + " not present in hyp."
+ + "\n"
+ )
+
+
+def compute_wer_by_line(hyp, ref):
+ hyp = list(map(lambda x: x.lower(), hyp))
+ ref = list(map(lambda x: x.lower(), ref))
+
+ len_hyp = len(hyp)
+ len_ref = len(ref)
+
+ cost_matrix = np.zeros((len_hyp + 1, len_ref + 1), dtype=np.int16)
+
+ ops_matrix = np.zeros((len_hyp + 1, len_ref + 1), dtype=np.int8)
+
+ for i in range(len_hyp + 1):
+ cost_matrix[i][0] = i
+ for j in range(len_ref + 1):
+ cost_matrix[0][j] = j
+
+ for i in range(1, len_hyp + 1):
+ for j in range(1, len_ref + 1):
+ if hyp[i - 1] == ref[j - 1]:
+ cost_matrix[i][j] = cost_matrix[i - 1][j - 1]
+ else:
+ substitution = cost_matrix[i - 1][j - 1] + 1
+ insertion = cost_matrix[i - 1][j] + 1
+ deletion = cost_matrix[i][j - 1] + 1
+
+ compare_val = [substitution, insertion, deletion]
+
+ min_val = min(compare_val)
+ operation_idx = compare_val.index(min_val) + 1
+ cost_matrix[i][j] = min_val
+ ops_matrix[i][j] = operation_idx
+
+ match_idx = []
+ i = len_hyp
+ j = len_ref
+ rst = {"nwords": len_ref, "cor": 0, "wrong": 0, "ins": 0, "del": 0, "sub": 0}
+ while i >= 0 or j >= 0:
+ i_idx = max(0, i)
+ j_idx = max(0, j)
+
+ if ops_matrix[i_idx][j_idx] == 0: # correct
+ if i - 1 >= 0 and j - 1 >= 0:
+ match_idx.append((j - 1, i - 1))
+ rst["cor"] += 1
+
+ i -= 1
+ j -= 1
+
+ elif ops_matrix[i_idx][j_idx] == 2: # insert
+ i -= 1
+ rst["ins"] += 1
+
+ elif ops_matrix[i_idx][j_idx] == 3: # delete
+ j -= 1
+ rst["del"] += 1
+
+ elif ops_matrix[i_idx][j_idx] == 1: # substitute
+ i -= 1
+ j -= 1
+ rst["sub"] += 1
+
+ if i < 0 and j >= 0:
+ rst["del"] += 1
+ elif j < 0 and i >= 0:
+ rst["ins"] += 1
+
+ match_idx.reverse()
+ wrong_cnt = cost_matrix[len_hyp][len_ref]
+ rst["wrong"] = wrong_cnt
+
+ return rst
+
+
+def print_cer_detail(rst):
+ return (
+ "("
+ + "nwords="
+ + str(rst["nwords"])
+ + ",cor="
+ + str(rst["cor"])
+ + ",ins="
+ + str(rst["ins"])
+ + ",del="
+ + str(rst["del"])
+ + ",sub="
+ + str(rst["sub"])
+ + ") corr:"
+ + "{:.2%}".format(rst["cor"] / rst["nwords"])
+ + ",cer:"
+ + "{:.2%}".format(rst["wrong"] / rst["nwords"])
+ )
+
+
+if __name__ == "__main__":
+ if len(sys.argv) != 4:
+ print("usage : python compute-wer.py test.ref test.hyp test.wer")
+ sys.exit(0)
+
+ ref_file = sys.argv[1]
+ hyp_file = sys.argv[2]
+ cer_detail_file = sys.argv[3]
+ compute_wer(ref_file, hyp_file, cer_detail_file)
diff --git a/almeval/models/stepaudio/funasr_detach/models/__init__.py b/almeval/models/stepaudio/funasr_detach/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/almeval/models/stepaudio/funasr_detach/models/bat/__init__.py b/almeval/models/stepaudio/funasr_detach/models/bat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/almeval/models/stepaudio/funasr_detach/models/bat/model.py b/almeval/models/stepaudio/funasr_detach/models/bat/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..2755fc2fccb4d0ed00f8e50976185a76f22084cb
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/bat/model.py
@@ -0,0 +1,37 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+import time
+import torch
+import logging
+from contextlib import contextmanager
+from typing import Dict, Optional, Tuple
+from distutils.version import LooseVersion
+
+from funasr_detach.register import tables
+from funasr_detach.utils import postprocess_utils
+from funasr_detach.utils.datadir_writer import DatadirWriter
+from funasr_detach.models.transducer.model import Transducer
+from funasr_detach.train_utils.device_funcs import force_gatherable
+from funasr_detach.models.transformer.scorers.ctc import CTCPrefixScorer
+from funasr_detach.losses.label_smoothing_loss import LabelSmoothingLoss
+from funasr_detach.models.transformer.scorers.length_bonus import LengthBonus
+from funasr_detach.models.transformer.utils.nets_utils import get_transducer_task_io
+from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank
+from funasr_detach.models.transducer.beam_search_transducer import BeamSearchTransducer
+
+
+if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
+ from torch.cuda.amp import autocast
+else:
+ # Nothing to do if torch<1.6.0
+ @contextmanager
+ def autocast(enabled=True):
+ yield
+
+
+@tables.register("model_classes", "BAT") # TODO: BAT training
+class BAT(Transducer):
+ pass
diff --git a/almeval/models/stepaudio/funasr_detach/models/bicif_paraformer/__init__.py b/almeval/models/stepaudio/funasr_detach/models/bicif_paraformer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/almeval/models/stepaudio/funasr_detach/models/bicif_paraformer/cif_predictor.py b/almeval/models/stepaudio/funasr_detach/models/bicif_paraformer/cif_predictor.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b0a229769e27c9002c34766c663bdcbb36c8f69
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/bicif_paraformer/cif_predictor.py
@@ -0,0 +1,417 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+import torch
+
+from funasr_detach.register import tables
+from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask
+
+
+class mae_loss(torch.nn.Module):
+
+ def __init__(self, normalize_length=False):
+ super(mae_loss, self).__init__()
+ self.normalize_length = normalize_length
+ self.criterion = torch.nn.L1Loss(reduction="sum")
+
+ def forward(self, token_length, pre_token_length):
+ loss_token_normalizer = token_length.size(0)
+ if self.normalize_length:
+ loss_token_normalizer = token_length.sum().type(torch.float32)
+ loss = self.criterion(token_length, pre_token_length)
+ loss = loss / loss_token_normalizer
+ return loss
+
+
+def cif(hidden, alphas, threshold):
+ batch_size, len_time, hidden_size = hidden.size()
+
+ # loop varss
+ integrate = torch.zeros([batch_size], device=hidden.device)
+ frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
+ # intermediate vars along time
+ list_fires = []
+ list_frames = []
+
+ for t in range(len_time):
+ alpha = alphas[:, t]
+ distribution_completion = (
+ torch.ones([batch_size], device=hidden.device) - integrate
+ )
+
+ integrate += alpha
+ list_fires.append(integrate)
+
+ fire_place = integrate >= threshold
+ integrate = torch.where(
+ fire_place,
+ integrate - torch.ones([batch_size], device=hidden.device),
+ integrate,
+ )
+ cur = torch.where(fire_place, distribution_completion, alpha)
+ remainds = alpha - cur
+
+ frame += cur[:, None] * hidden[:, t, :]
+ list_frames.append(frame)
+ frame = torch.where(
+ fire_place[:, None].repeat(1, hidden_size),
+ remainds[:, None] * hidden[:, t, :],
+ frame,
+ )
+
+ fires = torch.stack(list_fires, 1)
+ frames = torch.stack(list_frames, 1)
+ list_ls = []
+ len_labels = torch.round(alphas.sum(-1)).int()
+ max_label_len = len_labels.max()
+ for b in range(batch_size):
+ fire = fires[b, :]
+ l = torch.index_select(
+ frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze()
+ )
+ pad_l = torch.zeros(
+ [max_label_len - l.size(0), hidden_size], device=hidden.device
+ )
+ list_ls.append(torch.cat([l, pad_l], 0))
+ return torch.stack(list_ls, 0), fires
+
+
+def cif_wo_hidden(alphas, threshold):
+ batch_size, len_time = alphas.size()
+
+ # loop varss
+ integrate = torch.zeros([batch_size], device=alphas.device)
+ # intermediate vars along time
+ list_fires = []
+
+ for t in range(len_time):
+ alpha = alphas[:, t]
+
+ integrate += alpha
+ list_fires.append(integrate)
+
+ fire_place = integrate >= threshold
+ integrate = torch.where(
+ fire_place,
+ integrate - torch.ones([batch_size], device=alphas.device) * threshold,
+ integrate,
+ )
+
+ fires = torch.stack(list_fires, 1)
+ return fires
+
+
+@tables.register("predictor_classes", "CifPredictorV3")
+class CifPredictorV3(torch.nn.Module):
+ def __init__(
+ self,
+ idim,
+ l_order,
+ r_order,
+ threshold=1.0,
+ dropout=0.1,
+ smooth_factor=1.0,
+ noise_threshold=0,
+ tail_threshold=0.0,
+ tf2torch_tensor_name_prefix_torch="predictor",
+ tf2torch_tensor_name_prefix_tf="seq2seq/cif",
+ smooth_factor2=1.0,
+ noise_threshold2=0,
+ upsample_times=5,
+ upsample_type="cnn",
+ use_cif1_cnn=True,
+ tail_mask=True,
+ ):
+ super(CifPredictorV3, self).__init__()
+
+ self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0)
+ self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1)
+ self.cif_output = torch.nn.Linear(idim, 1)
+ self.dropout = torch.nn.Dropout(p=dropout)
+ self.threshold = threshold
+ self.smooth_factor = smooth_factor
+ self.noise_threshold = noise_threshold
+ self.tail_threshold = tail_threshold
+ self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
+ self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
+
+ self.upsample_times = upsample_times
+ self.upsample_type = upsample_type
+ self.use_cif1_cnn = use_cif1_cnn
+ if self.upsample_type == "cnn":
+ self.upsample_cnn = torch.nn.ConvTranspose1d(
+ idim, idim, self.upsample_times, self.upsample_times
+ )
+ self.cif_output2 = torch.nn.Linear(idim, 1)
+ elif self.upsample_type == "cnn_blstm":
+ self.upsample_cnn = torch.nn.ConvTranspose1d(
+ idim, idim, self.upsample_times, self.upsample_times
+ )
+ self.blstm = torch.nn.LSTM(
+ idim,
+ idim,
+ 1,
+ bias=True,
+ batch_first=True,
+ dropout=0.0,
+ bidirectional=True,
+ )
+ self.cif_output2 = torch.nn.Linear(idim * 2, 1)
+ elif self.upsample_type == "cnn_attn":
+ self.upsample_cnn = torch.nn.ConvTranspose1d(
+ idim, idim, self.upsample_times, self.upsample_times
+ )
+ from funasr_detach.models.transformer.encoder import (
+ EncoderLayer as TransformerEncoderLayer,
+ )
+ from funasr_detach.models.transformer.attention import MultiHeadedAttention
+ from funasr_detach.models.transformer.positionwise_feed_forward import (
+ PositionwiseFeedForward,
+ )
+
+ positionwise_layer_args = (
+ idim,
+ idim * 2,
+ 0.1,
+ )
+ self.self_attn = TransformerEncoderLayer(
+ idim,
+ MultiHeadedAttention(4, idim, 0.1),
+ PositionwiseFeedForward(*positionwise_layer_args),
+ 0.1,
+ True, # normalize_before,
+ False, # concat_after,
+ )
+ self.cif_output2 = torch.nn.Linear(idim, 1)
+ self.smooth_factor2 = smooth_factor2
+ self.noise_threshold2 = noise_threshold2
+
+ def forward(
+ self,
+ hidden,
+ target_label=None,
+ mask=None,
+ ignore_id=-1,
+ mask_chunk_predictor=None,
+ target_label_length=None,
+ ):
+ h = hidden
+ context = h.transpose(1, 2)
+ queries = self.pad(context)
+ output = torch.relu(self.cif_conv1d(queries))
+
+ # alphas2 is an extra head for timestamp prediction
+ if not self.use_cif1_cnn:
+ _output = context
+ else:
+ _output = output
+ if self.upsample_type == "cnn":
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1, 2)
+ elif self.upsample_type == "cnn_blstm":
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1, 2)
+ output2, (_, _) = self.blstm(output2)
+ elif self.upsample_type == "cnn_attn":
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1, 2)
+ output2, _ = self.self_attn(output2, mask)
+ # import pdb; pdb.set_trace()
+ alphas2 = torch.sigmoid(self.cif_output2(output2))
+ alphas2 = torch.nn.functional.relu(
+ alphas2 * self.smooth_factor2 - self.noise_threshold2
+ )
+ # repeat the mask in T demension to match the upsampled length
+ if mask is not None:
+ mask2 = (
+ mask.repeat(1, self.upsample_times, 1)
+ .transpose(-1, -2)
+ .reshape(alphas2.shape[0], -1)
+ )
+ mask2 = mask2.unsqueeze(-1)
+ alphas2 = alphas2 * mask2
+ alphas2 = alphas2.squeeze(-1)
+ token_num2 = alphas2.sum(-1)
+
+ output = output.transpose(1, 2)
+
+ output = self.cif_output(output)
+ alphas = torch.sigmoid(output)
+ alphas = torch.nn.functional.relu(
+ alphas * self.smooth_factor - self.noise_threshold
+ )
+ if mask is not None:
+ mask = mask.transpose(-1, -2).float()
+ alphas = alphas * mask
+ if mask_chunk_predictor is not None:
+ alphas = alphas * mask_chunk_predictor
+ alphas = alphas.squeeze(-1)
+ mask = mask.squeeze(-1)
+ if target_label_length is not None:
+ target_length = target_label_length
+ elif target_label is not None:
+ target_length = (target_label != ignore_id).float().sum(-1)
+ else:
+ target_length = None
+ token_num = alphas.sum(-1)
+
+ if target_length is not None:
+ alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
+ elif self.tail_threshold > 0.0:
+ hidden, alphas, token_num = self.tail_process_fn(
+ hidden, alphas, token_num, mask=mask
+ )
+
+ acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
+ if target_length is None and self.tail_threshold > 0.0:
+ token_num_int = torch.max(token_num).type(torch.int32).item()
+ acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
+ return acoustic_embeds, token_num, alphas, cif_peak, token_num2
+
+ def get_upsample_timestamp(self, hidden, mask=None, token_num=None):
+ h = hidden
+ b = hidden.shape[0]
+ context = h.transpose(1, 2)
+ queries = self.pad(context)
+ output = torch.relu(self.cif_conv1d(queries))
+
+ # alphas2 is an extra head for timestamp prediction
+ if not self.use_cif1_cnn:
+ _output = context
+ else:
+ _output = output
+ if self.upsample_type == "cnn":
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1, 2)
+ elif self.upsample_type == "cnn_blstm":
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1, 2)
+ output2, (_, _) = self.blstm(output2)
+ elif self.upsample_type == "cnn_attn":
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1, 2)
+ output2, _ = self.self_attn(output2, mask)
+ alphas2 = torch.sigmoid(self.cif_output2(output2))
+ alphas2 = torch.nn.functional.relu(
+ alphas2 * self.smooth_factor2 - self.noise_threshold2
+ )
+ # repeat the mask in T demension to match the upsampled length
+ if mask is not None:
+ mask2 = (
+ mask.repeat(1, self.upsample_times, 1)
+ .transpose(-1, -2)
+ .reshape(alphas2.shape[0], -1)
+ )
+ mask2 = mask2.unsqueeze(-1)
+ alphas2 = alphas2 * mask2
+ alphas2 = alphas2.squeeze(-1)
+ _token_num = alphas2.sum(-1)
+ if token_num is not None:
+ alphas2 *= (token_num / _token_num)[:, None].repeat(1, alphas2.size(1))
+ # re-downsample
+ ds_alphas = alphas2.reshape(b, -1, self.upsample_times).sum(-1)
+ ds_cif_peak = cif_wo_hidden(ds_alphas, self.threshold - 1e-4)
+ # upsampled alphas and cif_peak
+ us_alphas = alphas2
+ us_cif_peak = cif_wo_hidden(us_alphas, self.threshold - 1e-4)
+ return ds_alphas, ds_cif_peak, us_alphas, us_cif_peak
+
+ def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
+ b, t, d = hidden.size()
+ tail_threshold = self.tail_threshold
+ if mask is not None:
+ zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
+ ones_t = torch.ones_like(zeros_t)
+ mask_1 = torch.cat([mask, zeros_t], dim=1)
+ mask_2 = torch.cat([ones_t, mask], dim=1)
+ mask = mask_2 - mask_1
+ tail_threshold = mask * tail_threshold
+ alphas = torch.cat([alphas, zeros_t], dim=1)
+ alphas = torch.add(alphas, tail_threshold)
+ else:
+ tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(
+ alphas.device
+ )
+ tail_threshold = torch.reshape(tail_threshold, (1, 1))
+ alphas = torch.cat([alphas, tail_threshold], dim=1)
+ zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
+ hidden = torch.cat([hidden, zeros], dim=1)
+ token_num = alphas.sum(dim=-1)
+ token_num_floor = torch.floor(token_num)
+
+ return hidden, alphas, token_num_floor
+
+ def gen_frame_alignments(
+ self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None
+ ):
+ batch_size, maximum_length = alphas.size()
+ int_type = torch.int32
+
+ is_training = self.training
+ if is_training:
+ token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type)
+ else:
+ token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type)
+
+ max_token_num = torch.max(token_num).item()
+
+ alphas_cumsum = torch.cumsum(alphas, dim=1)
+ alphas_cumsum = torch.floor(alphas_cumsum).type(int_type)
+ alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1)
+
+ index = torch.ones([batch_size, max_token_num], dtype=int_type)
+ index = torch.cumsum(index, dim=1)
+ index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device)
+
+ index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type)
+ index_div_bool_zeros = index_div.eq(0)
+ index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1
+ index_div_bool_zeros_count = torch.clamp(
+ index_div_bool_zeros_count, 0, encoder_sequence_length.max()
+ )
+ token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to(
+ token_num.device
+ )
+ index_div_bool_zeros_count *= token_num_mask
+
+ index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat(
+ 1, 1, maximum_length
+ )
+ ones = torch.ones_like(index_div_bool_zeros_count_tile)
+ zeros = torch.zeros_like(index_div_bool_zeros_count_tile)
+ ones = torch.cumsum(ones, dim=2)
+ cond = index_div_bool_zeros_count_tile == ones
+ index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones)
+
+ index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type(
+ torch.bool
+ )
+ index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type(
+ int_type
+ )
+ index_div_bool_zeros_count_tile_out = torch.sum(
+ index_div_bool_zeros_count_tile, dim=1
+ )
+ index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type(
+ int_type
+ )
+ predictor_mask = (
+ (
+ ~make_pad_mask(
+ encoder_sequence_length, maxlen=encoder_sequence_length.max()
+ )
+ )
+ .type(int_type)
+ .to(encoder_sequence_length.device)
+ )
+ index_div_bool_zeros_count_tile_out = (
+ index_div_bool_zeros_count_tile_out * predictor_mask
+ )
+
+ predictor_alignments = index_div_bool_zeros_count_tile_out
+ predictor_alignments_length = predictor_alignments.sum(-1).type(
+ encoder_sequence_length.dtype
+ )
+ return predictor_alignments.detach(), predictor_alignments_length.detach()
diff --git a/almeval/models/stepaudio/funasr_detach/models/bicif_paraformer/model.py b/almeval/models/stepaudio/funasr_detach/models/bicif_paraformer/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbf078c2896ac16c13fd402f0573ee09f22ab351
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/bicif_paraformer/model.py
@@ -0,0 +1,395 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+import copy
+import time
+import torch
+import logging
+from contextlib import contextmanager
+from distutils.version import LooseVersion
+from typing import Dict, List, Optional, Tuple
+
+from funasr_detach.register import tables
+from funasr_detach.models.ctc.ctc import CTC
+from funasr_detach.utils import postprocess_utils
+from funasr_detach.metrics.compute_acc import th_accuracy
+from funasr_detach.utils.datadir_writer import DatadirWriter
+from funasr_detach.models.paraformer.model import Paraformer
+from funasr_detach.models.paraformer.search import Hypothesis
+from funasr_detach.train_utils.device_funcs import force_gatherable
+from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos
+from funasr_detach.utils.timestamp_tools import ts_prediction_lfr6_standard
+from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask, pad_list
+from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank
+
+
+if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
+ from torch.cuda.amp import autocast
+else:
+ # Nothing to do if torch<1.6.0
+ @contextmanager
+ def autocast(enabled=True):
+ yield
+
+
+@tables.register("model_classes", "BiCifParaformer")
+class BiCifParaformer(Paraformer):
+ """
+ Author: Speech Lab of DAMO Academy, Alibaba Group
+ Paper1: FunASR: A Fundamental End-to-End Speech Recognition Toolkit
+ https://arxiv.org/abs/2305.11013
+ Paper2: Achieving timestamp prediction while recognizing with non-autoregressive end-to-end ASR model
+ https://arxiv.org/abs/2301.12343
+ """
+
+ def __init__(
+ self,
+ *args,
+ **kwargs,
+ ):
+ super().__init__(*args, **kwargs)
+
+ def _calc_pre2_loss(
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
+ ):
+ encoder_out_mask = (
+ ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
+ ).to(encoder_out.device)
+ if self.predictor_bias == 1:
+ _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
+ ys_pad_lens = ys_pad_lens + self.predictor_bias
+ _, _, _, _, pre_token_length2 = self.predictor(
+ encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id
+ )
+
+ # loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
+ loss_pre2 = self.criterion_pre(
+ ys_pad_lens.type_as(pre_token_length2), pre_token_length2
+ )
+
+ return loss_pre2
+
+ def _calc_att_loss(
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
+ ):
+ encoder_out_mask = (
+ ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
+ ).to(encoder_out.device)
+ if self.predictor_bias == 1:
+ _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
+ ys_pad_lens = ys_pad_lens + self.predictor_bias
+ pre_acoustic_embeds, pre_token_length, _, pre_peak_index, _ = self.predictor(
+ encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id
+ )
+
+ # 0. sampler
+ decoder_out_1st = None
+ if self.sampling_ratio > 0.0:
+ sematic_embeds, decoder_out_1st = self.sampler(
+ encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds
+ )
+ else:
+ sematic_embeds = pre_acoustic_embeds
+
+ # 1. Forward decoder
+ decoder_outs = self.decoder(
+ encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
+ )
+ decoder_out, _ = decoder_outs[0], decoder_outs[1]
+
+ if decoder_out_1st is None:
+ decoder_out_1st = decoder_out
+ # 2. Compute attention loss
+ loss_att = self.criterion_att(decoder_out, ys_pad)
+ acc_att = th_accuracy(
+ decoder_out_1st.view(-1, self.vocab_size),
+ ys_pad,
+ ignore_label=self.ignore_id,
+ )
+ loss_pre = self.criterion_pre(
+ ys_pad_lens.type_as(pre_token_length), pre_token_length
+ )
+
+ # Compute cer/wer using attention-decoder
+ if self.training or self.error_calculator is None:
+ cer_att, wer_att = None, None
+ else:
+ ys_hat = decoder_out_1st.argmax(dim=-1)
+ cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
+
+ return loss_att, acc_att, cer_att, wer_att, loss_pre
+
+ def calc_predictor(self, encoder_out, encoder_out_lens):
+ encoder_out_mask = (
+ ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
+ ).to(encoder_out.device)
+ (
+ pre_acoustic_embeds,
+ pre_token_length,
+ alphas,
+ pre_peak_index,
+ pre_token_length2,
+ ) = self.predictor(
+ encoder_out, None, encoder_out_mask, ignore_id=self.ignore_id
+ )
+ return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
+
+ def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
+ encoder_out_mask = (
+ ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
+ ).to(encoder_out.device)
+ ds_alphas, ds_cif_peak, us_alphas, us_peaks = (
+ self.predictor.get_upsample_timestamp(
+ encoder_out, encoder_out_mask, token_num
+ )
+ )
+ return ds_alphas, ds_cif_peak, us_alphas, us_peaks
+
+ def forward(
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ text: torch.Tensor,
+ text_lengths: torch.Tensor,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
+ """Frontend + Encoder + Decoder + Calc loss
+ Args:
+ speech: (Batch, Length, ...)
+ speech_lengths: (Batch, )
+ text: (Batch, Length)
+ text_lengths: (Batch,)
+ """
+ if len(text_lengths.size()) > 1:
+ text_lengths = text_lengths[:, 0]
+ if len(speech_lengths.size()) > 1:
+ speech_lengths = speech_lengths[:, 0]
+
+ batch_size = speech.shape[0]
+
+ # Encoder
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+
+ loss_ctc, cer_ctc = None, None
+ loss_pre = None
+ stats = dict()
+
+ # decoder: CTC branch
+ if self.ctc_weight != 0.0:
+ loss_ctc, cer_ctc = self._calc_ctc_loss(
+ encoder_out, encoder_out_lens, text, text_lengths
+ )
+
+ # Collect CTC branch stats
+ stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
+ stats["cer_ctc"] = cer_ctc
+
+ # decoder: Attention decoder branch
+ loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_loss(
+ encoder_out, encoder_out_lens, text, text_lengths
+ )
+
+ loss_pre2 = self._calc_pre2_loss(
+ encoder_out, encoder_out_lens, text, text_lengths
+ )
+
+ # 3. CTC-Att loss definition
+ if self.ctc_weight == 0.0:
+ loss = (
+ loss_att
+ + loss_pre * self.predictor_weight
+ + loss_pre2 * self.predictor_weight * 0.5
+ )
+ else:
+ loss = (
+ self.ctc_weight * loss_ctc
+ + (1 - self.ctc_weight) * loss_att
+ + loss_pre * self.predictor_weight
+ + loss_pre2 * self.predictor_weight * 0.5
+ )
+
+ # Collect Attn branch stats
+ stats["loss_att"] = loss_att.detach() if loss_att is not None else None
+ stats["acc"] = acc_att
+ stats["cer"] = cer_att
+ stats["wer"] = wer_att
+ stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
+ stats["loss_pre2"] = loss_pre2.detach().cpu()
+
+ stats["loss"] = torch.clone(loss.detach())
+
+ # force_gatherable: to-device and to-tensor if scalar for DataParallel
+ if self.length_normalized_loss:
+ batch_size = int((text_lengths + self.predictor_bias).sum())
+
+ loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
+ return loss, stats, weight
+
+ def inference(
+ self,
+ data_in,
+ data_lengths=None,
+ key: list = None,
+ tokenizer=None,
+ frontend=None,
+ **kwargs,
+ ):
+
+ # init beamsearch
+ is_use_ctc = (
+ kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
+ )
+ is_use_lm = (
+ kwargs.get("lm_weight", 0.0) > 0.00001
+ and kwargs.get("lm_file", None) is not None
+ )
+ if self.beam_search is None and (is_use_lm or is_use_ctc):
+ logging.info("enable beam_search")
+ self.init_beam_search(**kwargs)
+ self.nbest = kwargs.get("nbest", 1)
+
+ meta_data = {}
+ # if isinstance(data_in, torch.Tensor): # fbank
+ # speech, speech_lengths = data_in, data_lengths
+ # if len(speech.shape) < 3:
+ # speech = speech[None, :, :]
+ # if speech_lengths is None:
+ # speech_lengths = speech.shape[1]
+ # else:
+ # extract fbank feats
+ time1 = time.perf_counter()
+ audio_sample_list = load_audio_text_image_video(
+ data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000)
+ )
+ time2 = time.perf_counter()
+ meta_data["load_data"] = f"{time2 - time1:0.3f}"
+ speech, speech_lengths = extract_fbank(
+ audio_sample_list,
+ data_type=kwargs.get("data_type", "sound"),
+ frontend=frontend,
+ )
+ time3 = time.perf_counter()
+ meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+ meta_data["batch_data_time"] = (
+ speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
+ )
+
+ speech = speech.to(device=kwargs["device"])
+ speech_lengths = speech_lengths.to(device=kwargs["device"])
+
+ # Encoder
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+ if isinstance(encoder_out, tuple):
+ encoder_out = encoder_out[0]
+
+ # predictor
+ predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
+ pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = (
+ predictor_outs[0],
+ predictor_outs[1],
+ predictor_outs[2],
+ predictor_outs[3],
+ )
+ pre_token_length = pre_token_length.round().long()
+ if torch.max(pre_token_length) < 1:
+ return []
+ decoder_outs = self.cal_decoder_with_predictor(
+ encoder_out, encoder_out_lens, pre_acoustic_embeds, pre_token_length
+ )
+ decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
+
+ # BiCifParaformer, test no bias cif2
+ _, _, us_alphas, us_peaks = self.calc_predictor_timestamp(
+ encoder_out, encoder_out_lens, pre_token_length
+ )
+
+ results = []
+ b, n, d = decoder_out.size()
+ for i in range(b):
+ x = encoder_out[i, : encoder_out_lens[i], :]
+ am_scores = decoder_out[i, : pre_token_length[i], :]
+ if self.beam_search is not None:
+ nbest_hyps = self.beam_search(
+ x=x,
+ am_scores=am_scores,
+ maxlenratio=kwargs.get("maxlenratio", 0.0),
+ minlenratio=kwargs.get("minlenratio", 0.0),
+ )
+
+ nbest_hyps = nbest_hyps[: self.nbest]
+ else:
+
+ yseq = am_scores.argmax(dim=-1)
+ score = am_scores.max(dim=-1)[0]
+ score = torch.sum(score, dim=-1)
+ # pad with mask tokens to ensure compatibility with sos/eos tokens
+ yseq = torch.tensor(
+ [self.sos] + yseq.tolist() + [self.eos], device=yseq.device
+ )
+ nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
+ for nbest_idx, hyp in enumerate(nbest_hyps):
+ ibest_writer = None
+ if kwargs.get("output_dir") is not None:
+ if not hasattr(self, "writer"):
+ self.writer = DatadirWriter(kwargs.get("output_dir"))
+ ibest_writer = self.writer[f"{nbest_idx+1}best_recog"]
+
+ # remove sos/eos and get results
+ last_pos = -1
+ if isinstance(hyp.yseq, list):
+ token_int = hyp.yseq[1:last_pos]
+ else:
+ token_int = hyp.yseq[1:last_pos].tolist()
+
+ # remove blank symbol id, which is assumed to be 0
+ token_int = list(
+ filter(
+ lambda x: x != self.eos
+ and x != self.sos
+ and x != self.blank_id,
+ token_int,
+ )
+ )
+
+ if tokenizer is not None:
+ # Change integer-ids to tokens
+ token = tokenizer.ids2tokens(token_int)
+ text = tokenizer.tokens2text(token)
+
+ _, timestamp = ts_prediction_lfr6_standard(
+ us_alphas[i][: encoder_out_lens[i] * 3],
+ us_peaks[i][: encoder_out_lens[i] * 3],
+ copy.copy(token),
+ vad_offset=kwargs.get("begin_time", 0),
+ )
+
+ text_postprocessed, time_stamp_postprocessed, word_lists = (
+ postprocess_utils.sentence_postprocess(token, timestamp)
+ )
+
+ result_i = {
+ "key": key[i],
+ "text": text_postprocessed,
+ "timestamp": time_stamp_postprocessed,
+ }
+
+ if ibest_writer is not None:
+ ibest_writer["token"][key[i]] = " ".join(token)
+ # ibest_writer["text"][key[i]] = text
+ ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed
+ ibest_writer["text"][key[i]] = text_postprocessed
+ else:
+ result_i = {"key": key[i], "token_int": token_int}
+ results.append(result_i)
+
+ return results, meta_data
diff --git a/almeval/models/stepaudio/funasr_detach/models/bicif_paraformer/template.yaml b/almeval/models/stepaudio/funasr_detach/models/bicif_paraformer/template.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d2b0e0a48472ae4ea2c3cfb27f2acd5ffcbb0b29
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/bicif_paraformer/template.yaml
@@ -0,0 +1,134 @@
+# This is an example that demonstrates how to configure a model file.
+# You can modify the configuration according to your own requirements.
+
+# to print the register_table:
+# from funasr.register import tables
+# tables.print()
+
+# network architecture
+#model: funasr.models.paraformer.model:Paraformer
+model: BiCifParaformer
+model_conf:
+ ctc_weight: 0.0
+ lsm_weight: 0.1
+ length_normalized_loss: true
+ predictor_weight: 1.0
+ predictor_bias: 1
+ sampling_ratio: 0.75
+
+# encoder
+encoder: SANMEncoder
+encoder_conf:
+ output_size: 512
+ attention_heads: 4
+ linear_units: 2048
+ num_blocks: 50
+ dropout_rate: 0.1
+ positional_dropout_rate: 0.1
+ attention_dropout_rate: 0.1
+ input_layer: pe
+ pos_enc_class: SinusoidalPositionEncoder
+ normalize_before: true
+ kernel_size: 11
+ sanm_shfit: 0
+ selfattention_layer_type: sanm
+
+# decoder
+decoder: ParaformerSANMDecoder
+decoder_conf:
+ attention_heads: 4
+ linear_units: 2048
+ num_blocks: 16
+ dropout_rate: 0.1
+ positional_dropout_rate: 0.1
+ self_attention_dropout_rate: 0.1
+ src_attention_dropout_rate: 0.1
+ att_layer_num: 16
+ kernel_size: 11
+ sanm_shfit: 0
+
+predictor: CifPredictorV3
+predictor_conf:
+ idim: 512
+ threshold: 1.0
+ l_order: 1
+ r_order: 1
+ tail_threshold: 0.45
+ smooth_factor2: 0.25
+ noise_threshold2: 0.01
+ upsample_times: 3
+ use_cif1_cnn: false
+ upsample_type: cnn_blstm
+
+# frontend related
+frontend: WavFrontend
+frontend_conf:
+ fs: 16000
+ window: hamming
+ n_mels: 80
+ frame_length: 25
+ frame_shift: 10
+ lfr_m: 7
+ lfr_n: 6
+
+specaug: SpecAugLFR
+specaug_conf:
+ apply_time_warp: false
+ time_warp_window: 5
+ time_warp_mode: bicubic
+ apply_freq_mask: true
+ freq_mask_width_range:
+ - 0
+ - 30
+ lfr_rate: 6
+ num_freq_mask: 1
+ apply_time_mask: true
+ time_mask_width_range:
+ - 0
+ - 12
+ num_time_mask: 1
+
+train_conf:
+ accum_grad: 1
+ grad_clip: 5
+ max_epoch: 150
+ val_scheduler_criterion:
+ - valid
+ - acc
+ best_model_criterion:
+ - - valid
+ - acc
+ - max
+ keep_nbest_models: 10
+ log_interval: 50
+
+optim: adam
+optim_conf:
+ lr: 0.0005
+scheduler: warmuplr
+scheduler_conf:
+ warmup_steps: 30000
+
+dataset: AudioDataset
+dataset_conf:
+ index_ds: IndexDSJsonl
+ batch_sampler: DynamicBatchLocalShuffleSampler
+ batch_type: example # example or length
+ batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
+ max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length,
+ buffer_size: 500
+ shuffle: True
+ num_workers: 0
+
+tokenizer: CharTokenizer
+tokenizer_conf:
+ unk_symbol:
+ split_with_space: true
+
+
+ctc_conf:
+ dropout_rate: 0.0
+ ctc_type: builtin
+ reduce: true
+ ignore_nan_grad: true
+normalize: null
diff --git a/almeval/models/stepaudio/funasr_detach/models/campplus/__init__.py b/almeval/models/stepaudio/funasr_detach/models/campplus/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/almeval/models/stepaudio/funasr_detach/models/campplus/cluster_backend.py b/almeval/models/stepaudio/funasr_detach/models/campplus/cluster_backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..2506126a04aa1028022b93df961459a2050fe2dc
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/campplus/cluster_backend.py
@@ -0,0 +1,200 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+# Modified from 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker)
+
+import scipy
+import torch
+import sklearn
+import hdbscan
+import numpy as np
+
+from sklearn.cluster._kmeans import k_means
+
+
+class SpectralCluster:
+ r"""A spectral clustering mehtod using unnormalized Laplacian of affinity matrix.
+ This implementation is adapted from https://github.com/speechbrain/speechbrain.
+ """
+
+ def __init__(self, min_num_spks=1, max_num_spks=15, pval=0.022):
+ self.min_num_spks = min_num_spks
+ self.max_num_spks = max_num_spks
+ self.pval = pval
+
+ def __call__(self, X, oracle_num=None):
+ # Similarity matrix computation
+ sim_mat = self.get_sim_mat(X)
+
+ # Refining similarity matrix with pval
+ prunned_sim_mat = self.p_pruning(sim_mat)
+
+ # Symmetrization
+ sym_prund_sim_mat = 0.5 * (prunned_sim_mat + prunned_sim_mat.T)
+
+ # Laplacian calculation
+ laplacian = self.get_laplacian(sym_prund_sim_mat)
+
+ # Get Spectral Embeddings
+ emb, num_of_spk = self.get_spec_embs(laplacian, oracle_num)
+
+ # Perform clustering
+ labels = self.cluster_embs(emb, num_of_spk)
+
+ return labels
+
+ def get_sim_mat(self, X):
+ # Cosine similarities
+ M = sklearn.metrics.pairwise.cosine_similarity(X, X)
+ return M
+
+ def p_pruning(self, A):
+ if A.shape[0] * self.pval < 6:
+ pval = 6.0 / A.shape[0]
+ else:
+ pval = self.pval
+
+ n_elems = int((1 - pval) * A.shape[0])
+
+ # For each row in a affinity matrix
+ for i in range(A.shape[0]):
+ low_indexes = np.argsort(A[i, :])
+ low_indexes = low_indexes[0:n_elems]
+
+ # Replace smaller similarity values by 0s
+ A[i, low_indexes] = 0
+ return A
+
+ def get_laplacian(self, M):
+ M[np.diag_indices(M.shape[0])] = 0
+ D = np.sum(np.abs(M), axis=1)
+ D = np.diag(D)
+ L = D - M
+ return L
+
+ def get_spec_embs(self, L, k_oracle=None):
+ lambdas, eig_vecs = scipy.linalg.eigh(L)
+
+ if k_oracle is not None:
+ num_of_spk = k_oracle
+ else:
+ lambda_gap_list = self.getEigenGaps(
+ lambdas[self.min_num_spks - 1 : self.max_num_spks + 1]
+ )
+ num_of_spk = np.argmax(lambda_gap_list) + self.min_num_spks
+
+ emb = eig_vecs[:, :num_of_spk]
+ return emb, num_of_spk
+
+ def cluster_embs(self, emb, k):
+ _, labels, _ = k_means(emb, k)
+ return labels
+
+ def getEigenGaps(self, eig_vals):
+ eig_vals_gap_list = []
+ for i in range(len(eig_vals) - 1):
+ gap = float(eig_vals[i + 1]) - float(eig_vals[i])
+ eig_vals_gap_list.append(gap)
+ return eig_vals_gap_list
+
+
+class UmapHdbscan:
+ r"""
+ Reference:
+ - Siqi Zheng, Hongbin Suo. Reformulating Speaker Diarization as Community Detection With
+ Emphasis On Topological Structure. ICASSP2022
+ """
+
+ def __init__(
+ self,
+ n_neighbors=20,
+ n_components=60,
+ min_samples=10,
+ min_cluster_size=10,
+ metric="cosine",
+ ):
+ self.n_neighbors = n_neighbors
+ self.n_components = n_components
+ self.min_samples = min_samples
+ self.min_cluster_size = min_cluster_size
+ self.metric = metric
+
+ def __call__(self, X):
+ import umap.umap_ as umap
+
+ umap_X = umap.UMAP(
+ n_neighbors=self.n_neighbors,
+ min_dist=0.0,
+ n_components=min(self.n_components, X.shape[0] - 2),
+ metric=self.metric,
+ ).fit_transform(X)
+ labels = hdbscan.HDBSCAN(
+ min_samples=self.min_samples,
+ min_cluster_size=self.min_cluster_size,
+ allow_single_cluster=True,
+ ).fit_predict(umap_X)
+ return labels
+
+
+class ClusterBackend(torch.nn.Module):
+ r"""Perfom clustering for input embeddings and output the labels.
+ Args:
+ model_dir: A model dir.
+ model_config: The model config.
+ """
+
+ def __init__(self):
+ super().__init__()
+ self.model_config = {"merge_thr": 0.78}
+ # self.other_config = kwargs
+
+ self.spectral_cluster = SpectralCluster()
+ self.umap_hdbscan_cluster = UmapHdbscan()
+
+ def forward(self, X, **params):
+ # clustering and return the labels
+ k = params["oracle_num"] if "oracle_num" in params else None
+ assert (
+ len(X.shape) == 2
+ ), "modelscope error: the shape of input should be [N, C]"
+ if X.shape[0] < 20:
+ return np.zeros(X.shape[0], dtype="int")
+ if X.shape[0] < 2048 or k is not None:
+ # unexpected corner case
+ labels = self.spectral_cluster(X, k)
+ else:
+ labels = self.umap_hdbscan_cluster(X)
+
+ if k is None and "merge_thr" in self.model_config:
+ labels = self.merge_by_cos(labels, X, self.model_config["merge_thr"])
+
+ return labels
+
+ def merge_by_cos(self, labels, embs, cos_thr):
+ # merge the similar speakers by cosine similarity
+ assert cos_thr > 0 and cos_thr <= 1
+ while True:
+ spk_num = labels.max() + 1
+ if spk_num == 1:
+ break
+ spk_center = []
+ for i in range(spk_num):
+ spk_emb = embs[labels == i].mean(0)
+ spk_center.append(spk_emb)
+ assert len(spk_center) > 0
+ spk_center = np.stack(spk_center, axis=0)
+ norm_spk_center = spk_center / np.linalg.norm(
+ spk_center, axis=1, keepdims=True
+ )
+ affinity = np.matmul(norm_spk_center, norm_spk_center.T)
+ affinity = np.triu(affinity, 1)
+ spks = np.unravel_index(np.argmax(affinity), affinity.shape)
+ if affinity[spks] < cos_thr:
+ break
+ for i in range(len(labels)):
+ if labels[i] == spks[1]:
+ labels[i] = spks[0]
+ elif labels[i] > spks[1]:
+ labels[i] -= 1
+ return labels
diff --git a/almeval/models/stepaudio/funasr_detach/models/campplus/components.py b/almeval/models/stepaudio/funasr_detach/models/campplus/components.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f0daa13ff558db1e543b1b50669acf1977a443c
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/campplus/components.py
@@ -0,0 +1,310 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+# Modified from 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker)
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint as cp
+
+
+class BasicResBlock(torch.nn.Module):
+ expansion = 1
+
+ def __init__(self, in_planes, planes, stride=1):
+ super(BasicResBlock, self).__init__()
+ self.conv1 = torch.nn.Conv2d(
+ in_planes, planes, kernel_size=3, stride=(stride, 1), padding=1, bias=False
+ )
+ self.bn1 = torch.nn.BatchNorm2d(planes)
+ self.conv2 = torch.nn.Conv2d(
+ planes, planes, kernel_size=3, stride=1, padding=1, bias=False
+ )
+ self.bn2 = torch.nn.BatchNorm2d(planes)
+
+ self.shortcut = torch.nn.Sequential()
+ if stride != 1 or in_planes != self.expansion * planes:
+ self.shortcut = torch.nn.Sequential(
+ torch.nn.Conv2d(
+ in_planes,
+ self.expansion * planes,
+ kernel_size=1,
+ stride=(stride, 1),
+ bias=False,
+ ),
+ torch.nn.BatchNorm2d(self.expansion * planes),
+ )
+
+ def forward(self, x):
+ out = F.relu(self.bn1(self.conv1(x)))
+ out = self.bn2(self.conv2(out))
+ out += self.shortcut(x)
+ out = F.relu(out)
+ return out
+
+
+class FCM(torch.nn.Module):
+ def __init__(
+ self, block=BasicResBlock, num_blocks=[2, 2], m_channels=32, feat_dim=80
+ ):
+ super(FCM, self).__init__()
+ self.in_planes = m_channels
+ self.conv1 = torch.nn.Conv2d(
+ 1, m_channels, kernel_size=3, stride=1, padding=1, bias=False
+ )
+ self.bn1 = torch.nn.BatchNorm2d(m_channels)
+
+ self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
+ self.layer2 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
+
+ self.conv2 = torch.nn.Conv2d(
+ m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False
+ )
+ self.bn2 = torch.nn.BatchNorm2d(m_channels)
+ self.out_channels = m_channels * (feat_dim // 8)
+
+ def _make_layer(self, block, planes, num_blocks, stride):
+ strides = [stride] + [1] * (num_blocks - 1)
+ layers = []
+ for stride in strides:
+ layers.append(block(self.in_planes, planes, stride))
+ self.in_planes = planes * block.expansion
+ return torch.nn.Sequential(*layers)
+
+ def forward(self, x):
+ x = x.unsqueeze(1)
+ out = F.relu(self.bn1(self.conv1(x)))
+ out = self.layer1(out)
+ out = self.layer2(out)
+ out = F.relu(self.bn2(self.conv2(out)))
+
+ shape = out.shape
+ out = out.reshape(shape[0], shape[1] * shape[2], shape[3])
+ return out
+
+
+def get_nonlinear(config_str, channels):
+ nonlinear = torch.nn.Sequential()
+ for name in config_str.split("-"):
+ if name == "relu":
+ nonlinear.add_module("relu", torch.nn.ReLU(inplace=True))
+ elif name == "prelu":
+ nonlinear.add_module("prelu", torch.nn.PReLU(channels))
+ elif name == "batchnorm":
+ nonlinear.add_module("batchnorm", torch.nn.BatchNorm1d(channels))
+ elif name == "batchnorm_":
+ nonlinear.add_module(
+ "batchnorm", torch.nn.BatchNorm1d(channels, affine=False)
+ )
+ else:
+ raise ValueError("Unexpected module ({}).".format(name))
+ return nonlinear
+
+
+def statistics_pooling(x, dim=-1, keepdim=False, unbiased=True, eps=1e-2):
+ mean = x.mean(dim=dim)
+ std = x.std(dim=dim, unbiased=unbiased)
+ stats = torch.cat([mean, std], dim=-1)
+ if keepdim:
+ stats = stats.unsqueeze(dim=dim)
+ return stats
+
+
+class StatsPool(torch.nn.Module):
+ def forward(self, x):
+ return statistics_pooling(x)
+
+
+class TDNNLayer(torch.nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ padding=0,
+ dilation=1,
+ bias=False,
+ config_str="batchnorm-relu",
+ ):
+ super(TDNNLayer, self).__init__()
+ if padding < 0:
+ assert (
+ kernel_size % 2 == 1
+ ), "Expect equal paddings, but got even kernel size ({})".format(
+ kernel_size
+ )
+ padding = (kernel_size - 1) // 2 * dilation
+ self.linear = torch.nn.Conv1d(
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride=stride,
+ padding=padding,
+ dilation=dilation,
+ bias=bias,
+ )
+ self.nonlinear = get_nonlinear(config_str, out_channels)
+
+ def forward(self, x):
+ x = self.linear(x)
+ x = self.nonlinear(x)
+ return x
+
+
+class CAMLayer(torch.nn.Module):
+ def __init__(
+ self,
+ bn_channels,
+ out_channels,
+ kernel_size,
+ stride,
+ padding,
+ dilation,
+ bias,
+ reduction=2,
+ ):
+ super(CAMLayer, self).__init__()
+ self.linear_local = torch.nn.Conv1d(
+ bn_channels,
+ out_channels,
+ kernel_size,
+ stride=stride,
+ padding=padding,
+ dilation=dilation,
+ bias=bias,
+ )
+ self.linear1 = torch.nn.Conv1d(bn_channels, bn_channels // reduction, 1)
+ self.relu = torch.nn.ReLU(inplace=True)
+ self.linear2 = torch.nn.Conv1d(bn_channels // reduction, out_channels, 1)
+ self.sigmoid = torch.nn.Sigmoid()
+
+ def forward(self, x):
+ y = self.linear_local(x)
+ context = x.mean(-1, keepdim=True) + self.seg_pooling(x)
+ context = self.relu(self.linear1(context))
+ m = self.sigmoid(self.linear2(context))
+ return y * m
+
+ def seg_pooling(self, x, seg_len=100, stype="avg"):
+ if stype == "avg":
+ seg = F.avg_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
+ elif stype == "max":
+ seg = F.max_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
+ else:
+ raise ValueError("Wrong segment pooling type.")
+ shape = seg.shape
+ seg = seg.unsqueeze(-1).expand(*shape, seg_len).reshape(*shape[:-1], -1)
+ seg = seg[..., : x.shape[-1]]
+ return seg
+
+
+class CAMDenseTDNNLayer(torch.nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ bn_channels,
+ kernel_size,
+ stride=1,
+ dilation=1,
+ bias=False,
+ config_str="batchnorm-relu",
+ memory_efficient=False,
+ ):
+ super(CAMDenseTDNNLayer, self).__init__()
+ assert (
+ kernel_size % 2 == 1
+ ), "Expect equal paddings, but got even kernel size ({})".format(kernel_size)
+ padding = (kernel_size - 1) // 2 * dilation
+ self.memory_efficient = memory_efficient
+ self.nonlinear1 = get_nonlinear(config_str, in_channels)
+ self.linear1 = torch.nn.Conv1d(in_channels, bn_channels, 1, bias=False)
+ self.nonlinear2 = get_nonlinear(config_str, bn_channels)
+ self.cam_layer = CAMLayer(
+ bn_channels,
+ out_channels,
+ kernel_size,
+ stride=stride,
+ padding=padding,
+ dilation=dilation,
+ bias=bias,
+ )
+
+ def bn_function(self, x):
+ return self.linear1(self.nonlinear1(x))
+
+ def forward(self, x):
+ if self.training and self.memory_efficient:
+ x = cp.checkpoint(self.bn_function, x)
+ else:
+ x = self.bn_function(x)
+ x = self.cam_layer(self.nonlinear2(x))
+ return x
+
+
+class CAMDenseTDNNBlock(torch.nn.ModuleList):
+ def __init__(
+ self,
+ num_layers,
+ in_channels,
+ out_channels,
+ bn_channels,
+ kernel_size,
+ stride=1,
+ dilation=1,
+ bias=False,
+ config_str="batchnorm-relu",
+ memory_efficient=False,
+ ):
+ super(CAMDenseTDNNBlock, self).__init__()
+ for i in range(num_layers):
+ layer = CAMDenseTDNNLayer(
+ in_channels=in_channels + i * out_channels,
+ out_channels=out_channels,
+ bn_channels=bn_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ dilation=dilation,
+ bias=bias,
+ config_str=config_str,
+ memory_efficient=memory_efficient,
+ )
+ self.add_module("tdnnd%d" % (i + 1), layer)
+
+ def forward(self, x):
+ for layer in self:
+ x = torch.cat([x, layer(x)], dim=1)
+ return x
+
+
+class TransitLayer(torch.nn.Module):
+ def __init__(
+ self, in_channels, out_channels, bias=True, config_str="batchnorm-relu"
+ ):
+ super(TransitLayer, self).__init__()
+ self.nonlinear = get_nonlinear(config_str, in_channels)
+ self.linear = torch.nn.Conv1d(in_channels, out_channels, 1, bias=bias)
+
+ def forward(self, x):
+ x = self.nonlinear(x)
+ x = self.linear(x)
+ return x
+
+
+class DenseLayer(torch.nn.Module):
+ def __init__(
+ self, in_channels, out_channels, bias=False, config_str="batchnorm-relu"
+ ):
+ super(DenseLayer, self).__init__()
+ self.linear = torch.nn.Conv1d(in_channels, out_channels, 1, bias=bias)
+ self.nonlinear = get_nonlinear(config_str, out_channels)
+
+ def forward(self, x):
+ if len(x.shape) == 2:
+ x = self.linear(x.unsqueeze(dim=-1)).squeeze(dim=-1)
+ else:
+ x = self.linear(x)
+ x = self.nonlinear(x)
+ return x
diff --git a/almeval/models/stepaudio/funasr_detach/models/campplus/model.py b/almeval/models/stepaudio/funasr_detach/models/campplus/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd226e96f643f7ac01fe9476b7891a69dc5fd54f
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/campplus/model.py
@@ -0,0 +1,149 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+# Modified from 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker)
+
+import time
+import torch
+import numpy as np
+from collections import OrderedDict
+from contextlib import contextmanager
+from distutils.version import LooseVersion
+
+from funasr_detach.register import tables
+from funasr_detach.models.campplus.utils import extract_feature
+from funasr_detach.utils.load_utils import load_audio_text_image_video
+from funasr_detach.models.campplus.components import (
+ DenseLayer,
+ StatsPool,
+ TDNNLayer,
+ CAMDenseTDNNBlock,
+ TransitLayer,
+ get_nonlinear,
+ FCM,
+)
+
+
+if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
+ from torch.cuda.amp import autocast
+else:
+ # Nothing to do if torch<1.6.0
+ @contextmanager
+ def autocast(enabled=True):
+ yield
+
+
+@tables.register("model_classes", "CAMPPlus")
+class CAMPPlus(torch.nn.Module):
+ def __init__(
+ self,
+ feat_dim=80,
+ embedding_size=192,
+ growth_rate=32,
+ bn_size=4,
+ init_channels=128,
+ config_str="batchnorm-relu",
+ memory_efficient=True,
+ output_level="segment",
+ **kwargs,
+ ):
+ super().__init__()
+
+ self.head = FCM(feat_dim=feat_dim)
+ channels = self.head.out_channels
+ self.output_level = output_level
+
+ self.xvector = torch.nn.Sequential(
+ OrderedDict(
+ [
+ (
+ "tdnn",
+ TDNNLayer(
+ channels,
+ init_channels,
+ 5,
+ stride=2,
+ dilation=1,
+ padding=-1,
+ config_str=config_str,
+ ),
+ ),
+ ]
+ )
+ )
+ channels = init_channels
+ for i, (num_layers, kernel_size, dilation) in enumerate(
+ zip((12, 24, 16), (3, 3, 3), (1, 2, 2))
+ ):
+ block = CAMDenseTDNNBlock(
+ num_layers=num_layers,
+ in_channels=channels,
+ out_channels=growth_rate,
+ bn_channels=bn_size * growth_rate,
+ kernel_size=kernel_size,
+ dilation=dilation,
+ config_str=config_str,
+ memory_efficient=memory_efficient,
+ )
+ self.xvector.add_module("block%d" % (i + 1), block)
+ channels = channels + num_layers * growth_rate
+ self.xvector.add_module(
+ "transit%d" % (i + 1),
+ TransitLayer(
+ channels, channels // 2, bias=False, config_str=config_str
+ ),
+ )
+ channels //= 2
+
+ self.xvector.add_module("out_nonlinear", get_nonlinear(config_str, channels))
+
+ if self.output_level == "segment":
+ self.xvector.add_module("stats", StatsPool())
+ self.xvector.add_module(
+ "dense",
+ DenseLayer(channels * 2, embedding_size, config_str="batchnorm_"),
+ )
+ else:
+ assert (
+ self.output_level == "frame"
+ ), "`output_level` should be set to 'segment' or 'frame'. "
+
+ for m in self.modules():
+ if isinstance(m, (torch.nn.Conv1d, torch.nn.Linear)):
+ torch.nn.init.kaiming_normal_(m.weight.data)
+ if m.bias is not None:
+ torch.nn.init.zeros_(m.bias)
+
+ def forward(self, x):
+ x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T)
+ x = self.head(x)
+ x = self.xvector(x)
+ if self.output_level == "frame":
+ x = x.transpose(1, 2)
+ return x
+
+ def inference(
+ self,
+ data_in,
+ data_lengths=None,
+ key: list = None,
+ tokenizer=None,
+ frontend=None,
+ **kwargs,
+ ):
+ # extract fbank feats
+ meta_data = {}
+ time1 = time.perf_counter()
+ audio_sample_list = load_audio_text_image_video(
+ data_in, fs=16000, audio_fs=kwargs.get("fs", 16000), data_type="sound"
+ )
+ time2 = time.perf_counter()
+ meta_data["load_data"] = f"{time2 - time1:0.3f}"
+ speech, speech_lengths, speech_times = extract_feature(audio_sample_list)
+ speech = speech.to(device=kwargs["device"])
+ time3 = time.perf_counter()
+ meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+ meta_data["batch_data_time"] = np.array(speech_times).sum().item() / 16000.0
+ results = [{"spk_embedding": self.forward(speech.to(torch.float32))}]
+ return results, meta_data
diff --git a/almeval/models/stepaudio/funasr_detach/models/campplus/template.yaml b/almeval/models/stepaudio/funasr_detach/models/campplus/template.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..38dcfde3e1828f468446eac35df33a8bb8b95b6a
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/campplus/template.yaml
@@ -0,0 +1,23 @@
+# This is an example that demonstrates how to configure a model file.
+# You can modify the configuration according to your own requirements.
+
+# to print the register_table:
+# from funasr.register import tables
+# tables.print()
+
+# network architecture
+model: CAMPPlus
+model_conf:
+ feat_dim: 80
+ embedding_size: 192
+ growth_rate: 32
+ bn_size: 4
+ init_channels: 128
+ config_str: 'batchnorm-relu'
+ memory_efficient: True
+ output_level: 'segment'
+
+# frontend related
+frontend: WavFrontend
+frontend_conf:
+ fs: 16000
diff --git a/almeval/models/stepaudio/funasr_detach/models/transformer/scorers/__init__.py b/almeval/models/stepaudio/funasr_detach/models/transformer/scorers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7f177368e62a5578b8706300e101f831a3972ac
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/transformer/scorers/__init__.py
@@ -0,0 +1 @@
+"""Initialize sub package."""
diff --git a/almeval/models/stepaudio/funasr_detach/models/transformer/scorers/ctc_prefix_score.py b/almeval/models/stepaudio/funasr_detach/models/transformer/scorers/ctc_prefix_score.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c67ecd096de46ad00972cf3a8ba812852f38c97
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/transformer/scorers/ctc_prefix_score.py
@@ -0,0 +1,359 @@
+#!/usr/bin/env python3
+
+# Copyright 2018 Mitsubishi Electric Research Labs (Takaaki Hori)
+# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+import torch
+
+import numpy as np
+import six
+
+
+class CTCPrefixScoreTH(object):
+ """Batch processing of CTCPrefixScore
+
+ which is based on Algorithm 2 in WATANABE et al.
+ "HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION,"
+ but extended to efficiently compute the label probablities for multiple
+ hypotheses simultaneously
+ See also Seki et al. "Vectorized Beam Search for CTC-Attention-Based
+ Speech Recognition," In INTERSPEECH (pp. 3825-3829), 2019.
+ """
+
+ def __init__(self, x, xlens, blank, eos, margin=0):
+ """Construct CTC prefix scorer
+
+ :param torch.Tensor x: input label posterior sequences (B, T, O)
+ :param torch.Tensor xlens: input lengths (B,)
+ :param int blank: blank label id
+ :param int eos: end-of-sequence id
+ :param int margin: margin parameter for windowing (0 means no windowing)
+ """
+ # In the comment lines,
+ # we assume T: input_length, B: batch size, W: beam width, O: output dim.
+ self.logzero = -10000000000.0
+ self.blank = blank
+ self.eos = eos
+ self.batch = x.size(0)
+ self.input_length = x.size(1)
+ self.odim = x.size(2)
+ self.dtype = x.dtype
+ self.device = (
+ torch.device("cuda:%d" % x.get_device())
+ if x.is_cuda
+ else torch.device("cpu")
+ )
+ # Pad the rest of posteriors in the batch
+ # TODO(takaaki-hori): need a better way without for-loops
+ for i, l in enumerate(xlens):
+ if l < self.input_length:
+ x[i, l:, :] = self.logzero
+ x[i, l:, blank] = 0
+ # Reshape input x
+ xn = x.transpose(0, 1) # (B, T, O) -> (T, B, O)
+ xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim)
+ self.x = torch.stack([xn, xb]) # (2, T, B, O)
+ self.end_frames = torch.as_tensor(xlens) - 1
+
+ # Setup CTC windowing
+ self.margin = margin
+ if margin > 0:
+ self.frame_ids = torch.arange(
+ self.input_length, dtype=self.dtype, device=self.device
+ )
+ # Base indices for index conversion
+ self.idx_bh = None
+ self.idx_b = torch.arange(self.batch, device=self.device)
+ self.idx_bo = (self.idx_b * self.odim).unsqueeze(1)
+
+ def __call__(self, y, state, scoring_ids=None, att_w=None):
+ """Compute CTC prefix scores for next labels
+
+ :param list y: prefix label sequences
+ :param tuple state: previous CTC state
+ :param torch.Tensor pre_scores: scores for pre-selection of hypotheses (BW, O)
+ :param torch.Tensor att_w: attention weights to decide CTC window
+ :return new_state, ctc_local_scores (BW, O)
+ """
+ output_length = len(y[0]) - 1 # ignore sos
+ last_ids = [yi[-1] for yi in y] # last output label ids
+ n_bh = len(last_ids) # batch * hyps
+ n_hyps = n_bh // self.batch # assuming each utterance has the same # of hyps
+ self.scoring_num = scoring_ids.size(-1) if scoring_ids is not None else 0
+ # prepare state info
+ if state is None:
+ r_prev = torch.full(
+ (self.input_length, 2, self.batch, n_hyps),
+ self.logzero,
+ dtype=self.dtype,
+ device=self.device,
+ )
+ r_prev[:, 1] = torch.cumsum(self.x[0, :, :, self.blank], 0).unsqueeze(2)
+ r_prev = r_prev.view(-1, 2, n_bh)
+ s_prev = 0.0
+ f_min_prev = 0
+ f_max_prev = 1
+ else:
+ r_prev, s_prev, f_min_prev, f_max_prev = state
+
+ # select input dimensions for scoring
+ if self.scoring_num > 0:
+ scoring_idmap = torch.full(
+ (n_bh, self.odim), -1, dtype=torch.long, device=self.device
+ )
+ snum = self.scoring_num
+ if self.idx_bh is None or n_bh > len(self.idx_bh):
+ self.idx_bh = torch.arange(n_bh, device=self.device).view(-1, 1)
+ scoring_idmap[self.idx_bh[:n_bh], scoring_ids] = torch.arange(
+ snum, device=self.device
+ )
+ scoring_idx = (
+ scoring_ids + self.idx_bo.repeat(1, n_hyps).view(-1, 1)
+ ).view(-1)
+ x_ = torch.index_select(
+ self.x.view(2, -1, self.batch * self.odim), 2, scoring_idx
+ ).view(2, -1, n_bh, snum)
+ else:
+ scoring_ids = None
+ scoring_idmap = None
+ snum = self.odim
+ x_ = self.x.unsqueeze(3).repeat(1, 1, 1, n_hyps, 1).view(2, -1, n_bh, snum)
+
+ # new CTC forward probs are prepared as a (T x 2 x BW x S) tensor
+ # that corresponds to r_t^n(h) and r_t^b(h) in a batch.
+ r = torch.full(
+ (self.input_length, 2, n_bh, snum),
+ self.logzero,
+ dtype=self.dtype,
+ device=self.device,
+ )
+ if output_length == 0:
+ r[0, 0] = x_[0, 0]
+
+ r_sum = torch.logsumexp(r_prev, 1)
+ log_phi = r_sum.unsqueeze(2).repeat(1, 1, snum)
+ if scoring_ids is not None:
+ for idx in range(n_bh):
+ pos = scoring_idmap[idx, last_ids[idx]]
+ if pos >= 0:
+ log_phi[:, idx, pos] = r_prev[:, 1, idx]
+ else:
+ for idx in range(n_bh):
+ log_phi[:, idx, last_ids[idx]] = r_prev[:, 1, idx]
+
+ # decide start and end frames based on attention weights
+ if att_w is not None and self.margin > 0:
+ f_arg = torch.matmul(att_w, self.frame_ids)
+ f_min = max(int(f_arg.min().cpu()), f_min_prev)
+ f_max = max(int(f_arg.max().cpu()), f_max_prev)
+ start = min(f_max_prev, max(f_min - self.margin, output_length, 1))
+ end = min(f_max + self.margin, self.input_length)
+ else:
+ f_min = f_max = 0
+ start = max(output_length, 1)
+ end = self.input_length
+
+ # compute forward probabilities log(r_t^n(h)) and log(r_t^b(h))
+ for t in range(start, end):
+ rp = r[t - 1]
+ rr = torch.stack([rp[0], log_phi[t - 1], rp[0], rp[1]]).view(
+ 2, 2, n_bh, snum
+ )
+ r[t] = torch.logsumexp(rr, 1) + x_[:, t]
+
+ # compute log prefix probabilities log(psi)
+ log_phi_x = torch.cat((log_phi[0].unsqueeze(0), log_phi[:-1]), dim=0) + x_[0]
+ if scoring_ids is not None:
+ log_psi = torch.full(
+ (n_bh, self.odim), self.logzero, dtype=self.dtype, device=self.device
+ )
+ log_psi_ = torch.logsumexp(
+ torch.cat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), dim=0),
+ dim=0,
+ )
+ for si in range(n_bh):
+ log_psi[si, scoring_ids[si]] = log_psi_[si]
+ else:
+ log_psi = torch.logsumexp(
+ torch.cat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), dim=0),
+ dim=0,
+ )
+
+ for si in range(n_bh):
+ log_psi[si, self.eos] = r_sum[self.end_frames[si // n_hyps], si]
+
+ # exclude blank probs
+ log_psi[:, self.blank] = self.logzero
+
+ return (log_psi - s_prev), (r, log_psi, f_min, f_max, scoring_idmap)
+
+ def index_select_state(self, state, best_ids):
+ """Select CTC states according to best ids
+
+ :param state : CTC state
+ :param best_ids : index numbers selected by beam pruning (B, W)
+ :return selected_state
+ """
+ r, s, f_min, f_max, scoring_idmap = state
+ # convert ids to BHO space
+ n_bh = len(s)
+ n_hyps = n_bh // self.batch
+ vidx = (best_ids + (self.idx_b * (n_hyps * self.odim)).view(-1, 1)).view(-1)
+ # select hypothesis scores
+ s_new = torch.index_select(s.view(-1), 0, vidx)
+ s_new = s_new.view(-1, 1).repeat(1, self.odim).view(n_bh, self.odim)
+ # convert ids to BHS space (S: scoring_num)
+ if scoring_idmap is not None:
+ snum = self.scoring_num
+ hyp_idx = (best_ids // self.odim + (self.idx_b * n_hyps).view(-1, 1)).view(
+ -1
+ )
+ label_ids = torch.fmod(best_ids, self.odim).view(-1)
+ score_idx = scoring_idmap[hyp_idx, label_ids]
+ score_idx[score_idx == -1] = 0
+ vidx = score_idx + hyp_idx * snum
+ else:
+ snum = self.odim
+ # select forward probabilities
+ r_new = torch.index_select(r.view(-1, 2, n_bh * snum), 2, vidx).view(
+ -1, 2, n_bh
+ )
+ return r_new, s_new, f_min, f_max
+
+ def extend_prob(self, x):
+ """Extend CTC prob.
+
+ :param torch.Tensor x: input label posterior sequences (B, T, O)
+ """
+
+ if self.x.shape[1] < x.shape[1]: # self.x (2,T,B,O); x (B,T,O)
+ # Pad the rest of posteriors in the batch
+ # TODO(takaaki-hori): need a better way without for-loops
+ xlens = [x.size(1)]
+ for i, l in enumerate(xlens):
+ if l < self.input_length:
+ x[i, l:, :] = self.logzero
+ x[i, l:, self.blank] = 0
+ tmp_x = self.x
+ xn = x.transpose(0, 1) # (B, T, O) -> (T, B, O)
+ xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim)
+ self.x = torch.stack([xn, xb]) # (2, T, B, O)
+ self.x[:, : tmp_x.shape[1], :, :] = tmp_x
+ self.input_length = x.size(1)
+ self.end_frames = torch.as_tensor(xlens) - 1
+
+ def extend_state(self, state):
+ """Compute CTC prefix state.
+
+
+ :param state : CTC state
+ :return ctc_state
+ """
+
+ if state is None:
+ # nothing to do
+ return state
+ else:
+ r_prev, s_prev, f_min_prev, f_max_prev = state
+
+ r_prev_new = torch.full(
+ (self.input_length, 2),
+ self.logzero,
+ dtype=self.dtype,
+ device=self.device,
+ )
+ start = max(r_prev.shape[0], 1)
+ r_prev_new[0:start] = r_prev
+ for t in six.moves.range(start, self.input_length):
+ r_prev_new[t, 1] = r_prev_new[t - 1, 1] + self.x[0, t, :, self.blank]
+
+ return (r_prev_new, s_prev, f_min_prev, f_max_prev)
+
+
+class CTCPrefixScore(object):
+ """Compute CTC label sequence scores
+
+ which is based on Algorithm 2 in WATANABE et al.
+ "HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION,"
+ but extended to efficiently compute the probablities of multiple labels
+ simultaneously
+ """
+
+ def __init__(self, x, blank, eos, xp):
+ self.xp = xp
+ self.logzero = -10000000000.0
+ self.blank = blank
+ self.eos = eos
+ self.input_length = len(x)
+ self.x = x
+
+ def initial_state(self):
+ """Obtain an initial CTC state
+
+ :return: CTC state
+ """
+ # initial CTC state is made of a frame x 2 tensor that corresponds to
+ # r_t^n() and r_t^b(), where 0 and 1 of axis=1 represent
+ # superscripts n and b (non-blank and blank), respectively.
+ r = self.xp.full((self.input_length, 2), self.logzero, dtype=np.float32)
+ r[0, 1] = self.x[0, self.blank]
+ for i in six.moves.range(1, self.input_length):
+ r[i, 1] = r[i - 1, 1] + self.x[i, self.blank]
+ return r
+
+ def __call__(self, y, cs, r_prev):
+ """Compute CTC prefix scores for next labels
+
+ :param y : prefix label sequence
+ :param cs : array of next labels
+ :param r_prev: previous CTC state
+ :return ctc_scores, ctc_states
+ """
+ # initialize CTC states
+ output_length = len(y) - 1 # ignore sos
+ # new CTC states are prepared as a frame x (n or b) x n_labels tensor
+ # that corresponds to r_t^n(h) and r_t^b(h).
+ r = self.xp.ndarray((self.input_length, 2, len(cs)), dtype=np.float32)
+ xs = self.x[:, cs]
+ if output_length == 0:
+ r[0, 0] = xs[0]
+ r[0, 1] = self.logzero
+ else:
+ r[output_length - 1] = self.logzero
+
+ # prepare forward probabilities for the last label
+ r_sum = self.xp.logaddexp(
+ r_prev[:, 0], r_prev[:, 1]
+ ) # log(r_t^n(g) + r_t^b(g))
+ last = y[-1]
+ if output_length > 0 and last in cs:
+ log_phi = self.xp.ndarray((self.input_length, len(cs)), dtype=np.float32)
+ for i in six.moves.range(len(cs)):
+ log_phi[:, i] = r_sum if cs[i] != last else r_prev[:, 1]
+ else:
+ log_phi = r_sum
+
+ # compute forward probabilities log(r_t^n(h)), log(r_t^b(h)),
+ # and log prefix probabilities log(psi)
+ start = max(output_length, 1)
+ log_psi = r[start - 1, 0]
+ for t in six.moves.range(start, self.input_length):
+ r[t, 0] = self.xp.logaddexp(r[t - 1, 0], log_phi[t - 1]) + xs[t]
+ r[t, 1] = (
+ self.xp.logaddexp(r[t - 1, 0], r[t - 1, 1]) + self.x[t, self.blank]
+ )
+ log_psi = self.xp.logaddexp(log_psi, log_phi[t - 1] + xs[t])
+
+ # get P(...eos|X) that ends with the prefix itself
+ eos_pos = self.xp.where(cs == self.eos)[0]
+ if len(eos_pos) > 0:
+ log_psi[eos_pos] = r_sum[-1] # log(r_T^n(g) + r_T^b(g))
+
+ # exclude blank probs
+ blank_pos = self.xp.where(cs == self.blank)[0]
+ if len(blank_pos) > 0:
+ log_psi[blank_pos] = self.logzero
+
+ # return the log prefix probability and CTC states, where the label axis
+ # of the CTC states is moved to the first axis to slice it easily
+ return log_psi, self.xp.rollaxis(r, 2)
diff --git a/almeval/models/stepaudio/funasr_detach/models/transformer/scorers/length_bonus.py b/almeval/models/stepaudio/funasr_detach/models/transformer/scorers/length_bonus.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4ba0a47f0db1de45a616343c8919b979e4255ea
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/transformer/scorers/length_bonus.py
@@ -0,0 +1,64 @@
+"""Length bonus module."""
+
+from typing import Any
+from typing import List
+from typing import Tuple
+
+import torch
+
+from funasr_detach.models.transformer.scorers.scorer_interface import (
+ BatchScorerInterface,
+)
+
+
+class LengthBonus(BatchScorerInterface):
+ """Length bonus in beam search."""
+
+ def __init__(self, n_vocab: int):
+ """Initialize class.
+
+ Args:
+ n_vocab (int): The number of tokens in vocabulary for beam search
+
+ """
+ self.n = n_vocab
+
+ def score(self, y, state, x):
+ """Score new token.
+
+ Args:
+ y (torch.Tensor): 1D torch.int64 prefix tokens.
+ state: Scorer state for prefix tokens
+ x (torch.Tensor): 2D encoder feature that generates ys.
+
+ Returns:
+ tuple[torch.Tensor, Any]: Tuple of
+ torch.float32 scores for next token (n_vocab)
+ and None
+
+ """
+ return torch.tensor([1.0], device=x.device, dtype=x.dtype).expand(self.n), None
+
+ def batch_score(
+ self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor
+ ) -> Tuple[torch.Tensor, List[Any]]:
+ """Score new token batch.
+
+ Args:
+ ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
+ states (List[Any]): Scorer states for prefix tokens.
+ xs (torch.Tensor):
+ The encoder feature that generates ys (n_batch, xlen, n_feat).
+
+ Returns:
+ tuple[torch.Tensor, List[Any]]: Tuple of
+ batchfied scores for next token with shape of `(n_batch, n_vocab)`
+ and next state list for ys.
+
+ """
+ return (
+ torch.tensor([1.0], device=xs.device, dtype=xs.dtype).expand(
+ ys.shape[0], self.n
+ ),
+ None,
+ )
diff --git a/almeval/models/stepaudio/funasr_detach/models/transformer/scorers/scorer_interface.py b/almeval/models/stepaudio/funasr_detach/models/transformer/scorers/scorer_interface.py
new file mode 100644
index 0000000000000000000000000000000000000000..946ec6be317603d87cfd938cc096d11b7bcbfbdf
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/transformer/scorers/scorer_interface.py
@@ -0,0 +1,188 @@
+"""Scorer interface module."""
+
+from typing import Any
+from typing import List
+from typing import Tuple
+
+import torch
+import warnings
+
+
+class ScorerInterface:
+ """Scorer interface for beam search.
+
+ The scorer performs scoring of the all tokens in vocabulary.
+
+ Examples:
+ * Search heuristics
+ * :class:`espnet.nets.scorers.length_bonus.LengthBonus`
+ * Decoder networks of the sequence-to-sequence models
+ * :class:`espnet.nets.pytorch_backend.nets.transformer.decoder.Decoder`
+ * :class:`espnet.nets.pytorch_backend.nets.rnn.decoders.Decoder`
+ * Neural language models
+ * :class:`espnet.nets.pytorch_backend.lm.transformer.TransformerLM`
+ * :class:`espnet.nets.pytorch_backend.lm.default.DefaultRNNLM`
+ * :class:`espnet.nets.pytorch_backend.lm.seq_rnn.SequentialRNNLM`
+
+ """
+
+ def init_state(self, x: torch.Tensor) -> Any:
+ """Get an initial state for decoding (optional).
+
+ Args:
+ x (torch.Tensor): The encoded feature tensor
+
+ Returns: initial state
+
+ """
+ return None
+
+ def select_state(self, state: Any, i: int, new_id: int = None) -> Any:
+ """Select state with relative ids in the main beam search.
+
+ Args:
+ state: Decoder state for prefix tokens
+ i (int): Index to select a state in the main beam search
+ new_id (int): New label index to select a state if necessary
+
+ Returns:
+ state: pruned state
+
+ """
+ return None if state is None else state[i]
+
+ def score(
+ self, y: torch.Tensor, state: Any, x: torch.Tensor
+ ) -> Tuple[torch.Tensor, Any]:
+ """Score new token (required).
+
+ Args:
+ y (torch.Tensor): 1D torch.int64 prefix tokens.
+ state: Scorer state for prefix tokens
+ x (torch.Tensor): The encoder feature that generates ys.
+
+ Returns:
+ tuple[torch.Tensor, Any]: Tuple of
+ scores for next token that has a shape of `(n_vocab)`
+ and next state for ys
+
+ """
+ raise NotImplementedError
+
+ def final_score(self, state: Any) -> float:
+ """Score eos (optional).
+
+ Args:
+ state: Scorer state for prefix tokens
+
+ Returns:
+ float: final score
+
+ """
+ return 0.0
+
+
+class BatchScorerInterface(ScorerInterface):
+ """Batch scorer interface."""
+
+ def batch_init_state(self, x: torch.Tensor) -> Any:
+ """Get an initial state for decoding (optional).
+
+ Args:
+ x (torch.Tensor): The encoded feature tensor
+
+ Returns: initial state
+
+ """
+ return self.init_state(x)
+
+ def batch_score(
+ self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor
+ ) -> Tuple[torch.Tensor, List[Any]]:
+ """Score new token batch (required).
+
+ Args:
+ ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
+ states (List[Any]): Scorer states for prefix tokens.
+ xs (torch.Tensor):
+ The encoder feature that generates ys (n_batch, xlen, n_feat).
+
+ Returns:
+ tuple[torch.Tensor, List[Any]]: Tuple of
+ batchfied scores for next token with shape of `(n_batch, n_vocab)`
+ and next state list for ys.
+
+ """
+ warnings.warn(
+ "{} batch score is implemented through for loop not parallelized".format(
+ self.__class__.__name__
+ )
+ )
+ scores = list()
+ outstates = list()
+ for i, (y, state, x) in enumerate(zip(ys, states, xs)):
+ score, outstate = self.score(y, state, x)
+ outstates.append(outstate)
+ scores.append(score)
+ scores = torch.cat(scores, 0).view(ys.shape[0], -1)
+ return scores, outstates
+
+
+class PartialScorerInterface(ScorerInterface):
+ """Partial scorer interface for beam search.
+
+ The partial scorer performs scoring when non-partial scorer finished scoring,
+ and receives pre-pruned next tokens to score because it is too heavy to score
+ all the tokens.
+
+ Examples:
+ * Prefix search for connectionist-temporal-classification models
+ * :class:`espnet.nets.scorers.ctc.CTCPrefixScorer`
+
+ """
+
+ def score_partial(
+ self, y: torch.Tensor, next_tokens: torch.Tensor, state: Any, x: torch.Tensor
+ ) -> Tuple[torch.Tensor, Any]:
+ """Score new token (required).
+
+ Args:
+ y (torch.Tensor): 1D prefix token
+ next_tokens (torch.Tensor): torch.int64 next token to score
+ state: decoder state for prefix tokens
+ x (torch.Tensor): The encoder feature that generates ys
+
+ Returns:
+ tuple[torch.Tensor, Any]:
+ Tuple of a score tensor for y that has a shape `(len(next_tokens),)`
+ and next state for ys
+
+ """
+ raise NotImplementedError
+
+
+class BatchPartialScorerInterface(BatchScorerInterface, PartialScorerInterface):
+ """Batch partial scorer interface for beam search."""
+
+ def batch_score_partial(
+ self,
+ ys: torch.Tensor,
+ next_tokens: torch.Tensor,
+ states: List[Any],
+ xs: torch.Tensor,
+ ) -> Tuple[torch.Tensor, Any]:
+ """Score new token (required).
+
+ Args:
+ ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
+ next_tokens (torch.Tensor): torch.int64 tokens to score (n_batch, n_token).
+ states (List[Any]): Scorer states for prefix tokens.
+ xs (torch.Tensor):
+ The encoder feature that generates ys (n_batch, xlen, n_feat).
+
+ Returns:
+ tuple[torch.Tensor, Any]:
+ Tuple of a score tensor for ys that has a shape `(n_batch, n_vocab)`
+ and next states for ys
+ """
+ raise NotImplementedError
diff --git a/almeval/models/stepaudio/funasr_detach/models/whisper/__init__.py b/almeval/models/stepaudio/funasr_detach/models/whisper/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/almeval/models/stepaudio/funasr_detach/models/whisper/model.py b/almeval/models/stepaudio/funasr_detach/models/whisper/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..d921868df527f562a19d351923925e151cdb2f57
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/whisper/model.py
@@ -0,0 +1,306 @@
+from dataclasses import dataclass
+from typing import Dict
+from typing import Iterable, Optional
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+from torch import nn
+
+
+from funasr_detach.models.whisper.utils.decoding import (
+ detect_language as detect_language_function,
+ decode as decode_function,
+)
+from funasr_detach.register import tables
+
+
+@dataclass
+class ModelDimensions:
+ n_mels: int
+ n_audio_ctx: int
+ n_audio_state: int
+ n_audio_head: int
+ n_audio_layer: int
+ n_vocab: int
+ n_text_ctx: int
+ n_text_state: int
+ n_text_head: int
+ n_text_layer: int
+
+
+class LayerNorm(nn.LayerNorm):
+ def forward(self, x: Tensor) -> Tensor:
+ return super().forward(x.float()).type(x.dtype)
+
+
+class Linear(nn.Linear):
+ def forward(self, x: Tensor) -> Tensor:
+ return F.linear(
+ x,
+ self.weight.to(x.dtype),
+ None if self.bias is None else self.bias.to(x.dtype),
+ )
+
+
+class Conv1d(nn.Conv1d):
+ def _conv_forward(
+ self, x: Tensor, weight: Tensor, bias: Optional[Tensor]
+ ) -> Tensor:
+ return super()._conv_forward(
+ x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
+ )
+
+
+def sinusoids(length, channels, max_timescale=10000):
+ """Returns sinusoids for positional embedding"""
+ assert channels % 2 == 0
+ log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+ inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
+ scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+ return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
+
+
+class MultiHeadAttention(nn.Module):
+ def __init__(self, n_state: int, n_head: int):
+ super().__init__()
+ self.n_head = n_head
+ self.query = Linear(n_state, n_state)
+ self.key = Linear(n_state, n_state, bias=False)
+ self.value = Linear(n_state, n_state)
+ self.out = Linear(n_state, n_state)
+
+ def forward(
+ self,
+ x: Tensor,
+ xa: Optional[Tensor] = None,
+ mask: Optional[Tensor] = None,
+ kv_cache: Optional[dict] = None,
+ ):
+ q = self.query(x)
+
+ if kv_cache is None or xa is None or self.key not in kv_cache:
+ # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
+ # otherwise, perform key/value projections for self- or cross-attention as usual.
+ k = self.key(x if xa is None else xa)
+ v = self.value(x if xa is None else xa)
+ else:
+ # for cross-attention, calculate keys and values once and reuse in subsequent calls.
+ k = kv_cache[self.key]
+ v = kv_cache[self.value]
+
+ wv, qk = self.qkv_attention(q, k, v, mask)
+ return self.out(wv), qk
+
+ def qkv_attention(
+ self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None
+ ):
+ n_batch, n_ctx, n_state = q.shape
+ scale = (n_state // self.n_head) ** -0.25
+ q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
+ k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
+ v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
+
+ qk = q @ k
+ if mask is not None:
+ qk = qk + mask[:n_ctx, :n_ctx]
+ qk = qk.float()
+
+ w = F.softmax(qk, dim=-1).to(q.dtype)
+ return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
+
+
+class ResidualAttentionBlock(nn.Module):
+ def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
+ super().__init__()
+
+ self.attn = MultiHeadAttention(n_state, n_head)
+ self.attn_ln = LayerNorm(n_state)
+
+ self.cross_attn = (
+ MultiHeadAttention(n_state, n_head) if cross_attention else None
+ )
+ self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
+
+ n_mlp = n_state * 4
+ self.mlp = nn.Sequential(
+ Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)
+ )
+ self.mlp_ln = LayerNorm(n_state)
+
+ def forward(
+ self,
+ x: Tensor,
+ xa: Optional[Tensor] = None,
+ mask: Optional[Tensor] = None,
+ kv_cache: Optional[dict] = None,
+ ):
+ x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0]
+ if self.cross_attn:
+ x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0]
+ x = x + self.mlp(self.mlp_ln(x))
+ return x
+
+
+@tables.register("encoder_classes", "WhisperEncoder")
+class AudioEncoder(nn.Module):
+ def __init__(
+ self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int
+ ):
+ super().__init__()
+ self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
+ self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
+ self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
+
+ self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
+ [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
+ )
+ self.ln_post = LayerNorm(n_state)
+
+ def forward(self, x: Tensor):
+ """
+ x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
+ the mel spectrogram of the audio
+ """
+ x = F.gelu(self.conv1(x))
+ x = F.gelu(self.conv2(x))
+ x = x.permute(0, 2, 1)
+
+ assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape"
+ x = (x + self.positional_embedding).to(x.dtype)
+
+ for block in self.blocks:
+ x = block(x)
+
+ x = self.ln_post(x)
+ return x
+
+
+@tables.register("decoder_classes", "WhisperDecoder")
+class TextDecoder(nn.Module):
+ def __init__(
+ self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int
+ ):
+ super().__init__()
+
+ self.token_embedding = nn.Embedding(n_vocab, n_state)
+ self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
+
+ self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
+ [
+ ResidualAttentionBlock(n_state, n_head, cross_attention=True)
+ for _ in range(n_layer)
+ ]
+ )
+ self.ln = LayerNorm(n_state)
+
+ mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
+ self.register_buffer("mask", mask, persistent=False)
+
+ def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
+ """
+ x : torch.LongTensor, shape = (batch_size, <= n_ctx)
+ the text tokens
+ xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
+ the encoded audio features to be attended on
+ """
+ offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
+ x = (
+ self.token_embedding(x)
+ + self.positional_embedding[offset : offset + x.shape[-1]]
+ )
+ x = x.to(xa.dtype)
+
+ for block in self.blocks:
+ x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
+
+ x = self.ln(x)
+ logits = (
+ x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)
+ ).float()
+
+ return logits
+
+
+@tables.register("model_classes", "Whisper")
+class Whisper(nn.Module):
+ def __init__(self, dims: dict):
+ super().__init__()
+ dims = ModelDimensions(**dims)
+ self.dims = dims
+ self.sos = 1
+ self.eos = 1
+ self.encoder = AudioEncoder(
+ self.dims.n_mels,
+ self.dims.n_audio_ctx,
+ self.dims.n_audio_state,
+ self.dims.n_audio_head,
+ self.dims.n_audio_layer,
+ )
+ self.decoder = TextDecoder(
+ self.dims.n_vocab,
+ self.dims.n_text_ctx,
+ self.dims.n_text_state,
+ self.dims.n_text_head,
+ self.dims.n_text_layer,
+ )
+
+ def embed_audio(self, mel: torch.Tensor):
+ return self.encoder(mel)
+
+ def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
+ return self.decoder(tokens, audio_features)
+
+ def forward(
+ self, mel: torch.Tensor, tokens: torch.Tensor
+ ) -> Dict[str, torch.Tensor]:
+ return self.decoder(tokens, self.encoder(mel))
+
+ @property
+ def device(self):
+ return next(self.parameters()).device
+
+ @property
+ def is_multilingual(self):
+ return self.dims.n_vocab == 51865
+
+ def install_kv_cache_hooks(self, cache: Optional[dict] = None):
+ """
+ The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value
+ tensors calculated for the previous positions. This method returns a dictionary that stores
+ all caches, and the necessary hooks for the key and value projection modules that save the
+ intermediate tensors to be reused during later calculations.
+
+ Returns
+ -------
+ cache : Dict[nn.Module, torch.Tensor]
+ A dictionary object mapping the key/value projection modules to its cache
+ hooks : List[RemovableHandle]
+ List of PyTorch RemovableHandle objects to stop the hooks to be called
+ """
+ cache = {**cache} if cache is not None else {}
+ hooks = []
+
+ def save_to_cache(module, _, output):
+ if (
+ module not in cache
+ or output.shape[1] > self.decoder.positional_embedding.shape[0]
+ ):
+ cache[module] = (
+ output # save as-is, for the first token or cross attention
+ )
+ else:
+ cache[module] = torch.cat([cache[module], output], dim=1).detach()
+ return cache[module]
+
+ def install_hooks(layer: nn.Module):
+ if isinstance(layer, MultiHeadAttention):
+ hooks.append(layer.key.register_forward_hook(save_to_cache))
+ hooks.append(layer.value.register_forward_hook(save_to_cache))
+
+ self.decoder.apply(install_hooks)
+ return cache, hooks
+
+ detect_language = detect_language_function
+ decode = decode_function
diff --git a/almeval/models/stepaudio/funasr_detach/models/whisper/utils/__init__.py b/almeval/models/stepaudio/funasr_detach/models/whisper/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/almeval/models/stepaudio/funasr_detach/models/whisper/utils/assets/gpt2/merges.txt b/almeval/models/stepaudio/funasr_detach/models/whisper/utils/assets/gpt2/merges.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6636bda4a1fd7a63653dffb22683b8162c8de956
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/whisper/utils/assets/gpt2/merges.txt
@@ -0,0 +1,50001 @@
+#version: 0.2 - Trained by `huggingface/tokenizers`
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diff --git a/almeval/models/stepaudio/funasr_detach/models/whisper/utils/assets/multilingual/merges.txt b/almeval/models/stepaudio/funasr_detach/models/whisper/utils/assets/multilingual/merges.txt
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diff --git a/almeval/models/stepaudio/funasr_detach/models/whisper/utils/audio.py b/almeval/models/stepaudio/funasr_detach/models/whisper/utils/audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..327e6e8ef72fafa9046ae30b0e98d0a9ade466f3
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/whisper/utils/audio.py
@@ -0,0 +1,136 @@
+import os
+from functools import lru_cache
+from typing import Union
+
+try:
+ import ffmpeg
+except:
+ print("Please Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.")
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from funasr_detach.models.whisper.utils.utils import exact_div
+
+# hard-coded audio hyperparameters
+SAMPLE_RATE = 16000
+N_FFT = 400
+N_MELS = 80
+HOP_LENGTH = 160
+CHUNK_LENGTH = 30
+N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk
+N_FRAMES = exact_div(
+ N_SAMPLES, HOP_LENGTH
+) # 3000: number of frames in a mel spectrogram input
+
+
+def load_audio(file: str, sr: int = SAMPLE_RATE):
+ """
+ Open an audio file and read as mono waveform, resampling as necessary
+
+ Parameters
+ ----------
+ file: str
+ The audio file to open
+
+ sr: int
+ The sample rate to resample the audio if necessary
+
+ Returns
+ -------
+ A NumPy array containing the audio waveform, in float32 dtype.
+ """
+ try:
+ # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
+ # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
+ out, _ = (
+ ffmpeg.input(file, threads=0)
+ .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
+ .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
+ )
+ except ffmpeg.Error as e:
+ raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+
+ return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
+
+
+def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
+ """
+ Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
+ """
+ if torch.is_tensor(array):
+ if array.shape[axis] > length:
+ array = array.index_select(
+ dim=axis, index=torch.arange(length, device=array.device)
+ )
+
+ if array.shape[axis] < length:
+ pad_widths = [(0, 0)] * array.ndim
+ pad_widths[axis] = (0, length - array.shape[axis])
+ array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
+ else:
+ if array.shape[axis] > length:
+ array = array.take(indices=range(length), axis=axis)
+
+ if array.shape[axis] < length:
+ pad_widths = [(0, 0)] * array.ndim
+ pad_widths[axis] = (0, length - array.shape[axis])
+ array = np.pad(array, pad_widths)
+
+ return array
+
+
+@lru_cache(maxsize=None)
+def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
+ """
+ load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
+ Allows decoupling librosa dependency; saved using:
+
+ np.savez_compressed(
+ "mel_filters.npz",
+ mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
+ )
+ """
+ assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
+ with np.load(
+ os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")
+ ) as f:
+ return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
+
+
+def log_mel_spectrogram(
+ audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS
+):
+ """
+ Compute the log-Mel spectrogram of
+
+ Parameters
+ ----------
+ audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
+ The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
+
+ n_mels: int
+ The number of Mel-frequency filters, only 80 is supported
+
+ Returns
+ -------
+ torch.Tensor, shape = (80, n_frames)
+ A Tensor that contains the Mel spectrogram
+ """
+ if not torch.is_tensor(audio):
+ if isinstance(audio, str):
+ audio = load_audio(audio)
+ audio = torch.from_numpy(audio)
+
+ window = torch.hann_window(N_FFT).to(audio.device)
+ stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
+ magnitudes = stft[..., :-1].abs() ** 2
+
+ filters = mel_filters(audio.device, n_mels)
+ mel_spec = filters @ magnitudes
+
+ log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+ log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+ log_spec = (log_spec + 4.0) / 4.0
+ return log_spec
diff --git a/almeval/models/stepaudio/funasr_detach/models/whisper/utils/decoding.py b/almeval/models/stepaudio/funasr_detach/models/whisper/utils/decoding.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c5bf3c6d6c252499677416bac58fa56b2ab27e3
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/whisper/utils/decoding.py
@@ -0,0 +1,811 @@
+from dataclasses import dataclass, field
+from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+from torch.distributions import Categorical
+
+from funasr_detach.models.whisper.utils.audio import CHUNK_LENGTH
+from funasr_detach.models.whisper.utils.tokenizer import Tokenizer, get_tokenizer
+from funasr_detach.models.whisper.utils.utils import compression_ratio
+
+if TYPE_CHECKING:
+ from funasr_detach.models.whisper_models.model import Whisper
+
+
+@torch.no_grad()
+def detect_language(
+ model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None
+) -> Tuple[Tensor, List[dict]]:
+ """
+ Detect the spoken language in the audio, and return them as list of strings, along with the ids
+ of the most probable language tokens and the probability distribution over all language tokens.
+ This is performed outside the main decode loop in order to not interfere with kv-caching.
+
+ Returns
+ -------
+ language_tokens : Tensor, shape = (n_audio,)
+ ids of the most probable language tokens, which appears after the startoftranscript token.
+ language_probs : List[Dict[str, float]], length = n_audio
+ list of dictionaries containing the probability distribution over all languages.
+ """
+ if tokenizer is None:
+ tokenizer = get_tokenizer(model.is_multilingual)
+ if (
+ tokenizer.language is None
+ or tokenizer.language_token not in tokenizer.sot_sequence
+ ):
+ raise ValueError(
+ f"This model doesn't have language tokens so it can't perform lang id"
+ )
+
+ single = mel.ndim == 2
+ if single:
+ mel = mel.unsqueeze(0)
+
+ # skip encoder forward pass if already-encoded audio features were given
+ if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state):
+ mel = model.encoder(mel)
+
+ # forward pass using a single token, startoftranscript
+ n_audio = mel.shape[0]
+ x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1]
+ logits = model.logits(x, mel)[:, 0]
+
+ # collect detected languages; suppress all non-language tokens
+ mask = torch.ones(logits.shape[-1], dtype=torch.bool)
+ mask[list(tokenizer.all_language_tokens)] = False
+ logits[:, mask] = -np.inf
+ language_tokens = logits.argmax(dim=-1)
+ language_token_probs = logits.softmax(dim=-1).cpu()
+ language_probs = [
+ {
+ c: language_token_probs[i, j].item()
+ for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes)
+ }
+ for i in range(n_audio)
+ ]
+
+ if single:
+ language_tokens = language_tokens[0]
+ language_probs = language_probs[0]
+
+ return language_tokens, language_probs
+
+
+@dataclass(frozen=True)
+class DecodingOptions:
+ task: str = (
+ "transcribe" # whether to perform X->X "transcribe" or X->English "translate"
+ )
+ language: Optional[str] = (
+ None # language that the audio is in; uses detected language if None
+ )
+
+ # sampling-related options
+ temperature: float = 0.0
+ sample_len: Optional[int] = None # maximum number of tokens to sample
+ best_of: Optional[int] = (
+ None # number of independent samples to collect, when t > 0
+ )
+ beam_size: Optional[int] = None # number of beams in beam search, when t == 0
+ patience: Optional[float] = (
+ None # patience in beam search (https://arxiv.org/abs/2204.05424)
+ )
+
+ # options for ranking generations (either beams or best-of-N samples)
+ length_penalty: Optional[float] = (
+ None # "alpha" in Google NMT, None defaults to length norm
+ )
+
+ # prompt, prefix, and token suppression
+ prompt: Optional[Union[str, List[int]]] = (
+ None # text or tokens for the previous context
+ )
+ prefix: Optional[Union[str, List[int]]] = (
+ None # text or tokens to prefix the current context
+ )
+ suppress_blank: bool = True # this will suppress blank outputs
+
+ # list of tokens ids (or comma-separated token ids) to suppress
+ # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()`
+ suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1"
+
+ # timestamp sampling options
+ without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only
+ max_initial_timestamp: Optional[float] = (
+ 1.0 # the initial timestamp cannot be later than this
+ )
+
+ # implementation details
+ fp16: bool = True # use fp16 for most of the calculation
+
+
+@dataclass(frozen=True)
+class DecodingResult:
+ audio_features: Tensor
+ language: str
+ language_probs: Optional[Dict[str, float]] = None
+ tokens: List[int] = field(default_factory=list)
+ text: str = ""
+ avg_logprob: float = np.nan
+ no_speech_prob: float = np.nan
+ temperature: float = np.nan
+ compression_ratio: float = np.nan
+
+
+class Inference:
+ def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor:
+ """Perform a forward pass on the decoder and return per-token logits"""
+ raise NotImplementedError
+
+ def rearrange_kv_cache(self, source_indices) -> None:
+ """Update the key-value cache according to the updated beams"""
+ raise NotImplementedError
+
+ def cleanup_caching(self) -> None:
+ """Clean up any resources or hooks after decoding is finished"""
+ pass
+
+
+class PyTorchInference(Inference):
+ def __init__(self, model: "Whisper", initial_token_length: int):
+ self.model: "Whisper" = model
+ self.initial_token_length = initial_token_length
+ self.kv_cache = {}
+ self.hooks = []
+
+ def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor:
+ if not self.kv_cache:
+ self.kv_cache, self.hooks = self.model.install_kv_cache_hooks()
+
+ if tokens.shape[-1] > self.initial_token_length:
+ # only need to use the last token except in the first forward pass
+ tokens = tokens[:, -1:]
+
+ return self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache)
+
+ def cleanup_caching(self):
+ for hook in self.hooks:
+ hook.remove()
+
+ self.kv_cache = {}
+ self.hooks = []
+
+ def rearrange_kv_cache(self, source_indices):
+ for module, tensor in self.kv_cache.items():
+ # update the key/value cache to contain the selected sequences
+ self.kv_cache[module] = tensor[source_indices].detach()
+
+
+class SequenceRanker:
+ def rank(
+ self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]
+ ) -> List[int]:
+ """
+ Given a list of groups of samples and their cumulative log probabilities,
+ return the indices of the samples in each group to select as the final result
+ """
+ raise NotImplementedError
+
+
+class MaximumLikelihoodRanker(SequenceRanker):
+ """
+ Select the sample with the highest log probabilities, penalized using either
+ a simple length normalization or Google NMT paper's length penalty
+ """
+
+ def __init__(self, length_penalty: Optional[float]):
+ self.length_penalty = length_penalty
+
+ def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]):
+ def scores(logprobs, lengths):
+ result = []
+ for logprob, length in zip(logprobs, lengths):
+ if self.length_penalty is None:
+ penalty = length
+ else:
+ # from the Google NMT paper
+ penalty = ((5 + length) / 6) ** self.length_penalty
+ result.append(logprob / penalty)
+ return result
+
+ # get the sequence with the highest score
+ lengths = [[len(t) for t in s] for s in tokens]
+ return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)]
+
+
+class TokenDecoder:
+ def reset(self):
+ """Initialize any stateful variables for decoding a new sequence"""
+
+ def update(
+ self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor
+ ) -> Tuple[Tensor, bool]:
+ """Specify how to select the next token, based on the current trace and logits
+
+ Parameters
+ ----------
+ tokens : Tensor, shape = (n_batch, current_sequence_length)
+ all tokens in the context so far, including the prefix and sot_sequence tokens
+
+ logits : Tensor, shape = (n_batch, vocab_size)
+ per-token logits of the probability distribution at the current step
+
+ sum_logprobs : Tensor, shape = (n_batch)
+ cumulative log probabilities for each sequence
+
+ Returns
+ -------
+ tokens : Tensor, shape = (n_batch, current_sequence_length + 1)
+ the tokens, appended with the selected next token
+
+ completed : bool
+ True if all sequences has reached the end of text
+
+ """
+ raise NotImplementedError
+
+ def finalize(
+ self, tokens: Tensor, sum_logprobs: Tensor
+ ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]:
+ """Finalize search and return the final candidate sequences
+
+ Parameters
+ ----------
+ tokens : Tensor, shape = (n_audio, n_group, current_sequence_length)
+ all tokens in the context so far, including the prefix and sot_sequence
+
+ sum_logprobs : Tensor, shape = (n_audio, n_group)
+ cumulative log probabilities for each sequence
+
+ Returns
+ -------
+ tokens : Sequence[Sequence[Tensor]], length = n_audio
+ sequence of Tensors containing candidate token sequences, for each audio input
+
+ sum_logprobs : List[List[float]], length = n_audio
+ sequence of cumulative log probabilities corresponding to the above
+
+ """
+ raise NotImplementedError
+
+
+class GreedyDecoder(TokenDecoder):
+ def __init__(self, temperature: float, eot: int):
+ self.temperature = temperature
+ self.eot = eot
+
+ def update(
+ self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor
+ ) -> Tuple[Tensor, bool]:
+ temperature = self.temperature
+ if temperature == 0:
+ next_tokens = logits.argmax(dim=-1)
+ else:
+ next_tokens = Categorical(logits=logits / temperature).sample()
+
+ logprobs = F.log_softmax(logits.float(), dim=-1)
+ current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens]
+ sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot)
+
+ next_tokens[tokens[:, -1] == self.eot] = self.eot
+ tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1)
+
+ completed = (tokens[:, -1] == self.eot).all()
+ return tokens, completed
+
+ def finalize(self, tokens: Tensor, sum_logprobs: Tensor):
+ # make sure each sequence has at least one EOT token at the end
+ tokens = F.pad(tokens, (0, 1), value=self.eot)
+ return tokens, sum_logprobs.tolist()
+
+
+class BeamSearchDecoder(TokenDecoder):
+ def __init__(
+ self,
+ beam_size: int,
+ eot: int,
+ inference: Inference,
+ patience: Optional[float] = None,
+ ):
+ self.beam_size = beam_size
+ self.eot = eot
+ self.inference = inference
+ self.patience = patience or 1.0
+ self.max_candidates: int = round(beam_size * self.patience)
+ self.finished_sequences = None
+
+ assert (
+ self.max_candidates > 0
+ ), f"Invalid beam size ({beam_size}) or patience ({patience})"
+
+ def reset(self):
+ self.finished_sequences = None
+
+ def update(
+ self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor
+ ) -> Tuple[Tensor, bool]:
+ if tokens.shape[0] % self.beam_size != 0:
+ raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0")
+
+ n_audio = tokens.shape[0] // self.beam_size
+ if self.finished_sequences is None: # for the first update
+ self.finished_sequences = [{} for _ in range(n_audio)]
+
+ logprobs = F.log_softmax(logits.float(), dim=-1)
+ next_tokens, source_indices, finished_sequences = [], [], []
+ for i in range(n_audio):
+ scores, sources, finished = {}, {}, {}
+
+ # STEP 1: calculate the cumulative log probabilities for possible candidates
+ for j in range(self.beam_size):
+ idx = i * self.beam_size + j
+ prefix = tokens[idx].tolist()
+ for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)):
+ new_logprob = (sum_logprobs[idx] + logprob).item()
+ sequence = tuple(prefix + [token.item()])
+ scores[sequence] = new_logprob
+ sources[sequence] = idx
+
+ # STEP 2: rank the candidates and keep the top beam_size sequences for each audio
+ saved = 0
+ for sequence in sorted(scores, key=scores.get, reverse=True):
+ if sequence[-1] == self.eot:
+ finished[sequence] = scores[sequence]
+ else:
+ sum_logprobs[len(next_tokens)] = scores[sequence]
+ next_tokens.append(sequence)
+ source_indices.append(sources[sequence])
+
+ saved += 1
+ if saved == self.beam_size:
+ break
+
+ finished_sequences.append(finished)
+
+ tokens = torch.tensor(next_tokens, device=tokens.device)
+ self.inference.rearrange_kv_cache(source_indices)
+
+ # add newly finished sequences to self.finished_sequences
+ assert len(self.finished_sequences) == len(finished_sequences)
+ for previously_finished, newly_finished in zip(
+ self.finished_sequences, finished_sequences
+ ):
+ for seq in sorted(newly_finished, key=newly_finished.get, reverse=True):
+ if len(previously_finished) >= self.max_candidates:
+ break # the candidate list is full
+ previously_finished[seq] = newly_finished[seq]
+
+ # mark as completed if all audio has enough number of samples
+ completed = all(
+ len(sequences) >= self.max_candidates
+ for sequences in self.finished_sequences
+ )
+ return tokens, completed
+
+ def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor):
+ # collect all finished sequences, including patience, and add unfinished ones if not enough
+ sum_logprobs = sum_logprobs.cpu()
+ for i, sequences in enumerate(self.finished_sequences):
+ if (
+ len(sequences) < self.beam_size
+ ): # when not enough sequences are finished
+ for j in list(np.argsort(sum_logprobs[i]))[::-1]:
+ sequence = preceding_tokens[i, j].tolist() + [self.eot]
+ sequences[tuple(sequence)] = sum_logprobs[i][j].item()
+ if len(sequences) >= self.beam_size:
+ break
+
+ tokens: List[List[Tensor]] = [
+ [torch.tensor(seq) for seq in sequences.keys()]
+ for sequences in self.finished_sequences
+ ]
+ sum_logprobs: List[List[float]] = [
+ list(sequences.values()) for sequences in self.finished_sequences
+ ]
+ return tokens, sum_logprobs
+
+
+class LogitFilter:
+ def apply(self, logits: Tensor, tokens: Tensor) -> None:
+ """Apply any filtering or masking to logits in-place
+
+ Parameters
+ ----------
+ logits : Tensor, shape = (n_batch, vocab_size)
+ per-token logits of the probability distribution at the current step
+
+ tokens : Tensor, shape = (n_batch, current_sequence_length)
+ all tokens in the context so far, including the prefix and sot_sequence tokens
+
+ """
+ raise NotImplementedError
+
+
+class SuppressBlank(LogitFilter):
+ def __init__(self, tokenizer: Tokenizer, sample_begin: int):
+ self.tokenizer = tokenizer
+ self.sample_begin = sample_begin
+
+ def apply(self, logits: Tensor, tokens: Tensor):
+ if tokens.shape[1] == self.sample_begin:
+ logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf
+
+
+class SuppressTokens(LogitFilter):
+ def __init__(self, suppress_tokens: Sequence[int]):
+ self.suppress_tokens = list(suppress_tokens)
+
+ def apply(self, logits: Tensor, tokens: Tensor):
+ logits[:, self.suppress_tokens] = -np.inf
+
+
+class ApplyTimestampRules(LogitFilter):
+ def __init__(
+ self,
+ tokenizer: Tokenizer,
+ sample_begin: int,
+ max_initial_timestamp_index: Optional[int],
+ ):
+ self.tokenizer = tokenizer
+ self.sample_begin = sample_begin
+ self.max_initial_timestamp_index = max_initial_timestamp_index
+
+ def apply(self, logits: Tensor, tokens: Tensor):
+ # suppress <|notimestamps|> which is handled by without_timestamps
+ if self.tokenizer.no_timestamps is not None:
+ logits[:, self.tokenizer.no_timestamps] = -np.inf
+
+ # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
+ for k in range(tokens.shape[0]):
+ seq = [t for t in tokens[k, self.sample_begin :].tolist()]
+ last_was_timestamp = (
+ len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin
+ )
+ penultimate_was_timestamp = (
+ len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin
+ )
+
+ if last_was_timestamp:
+ if penultimate_was_timestamp: # has to be non-timestamp
+ logits[k, self.tokenizer.timestamp_begin :] = -np.inf
+ else: # cannot be normal text tokens
+ logits[k, : self.tokenizer.eot] = -np.inf
+
+ if tokens.shape[1] == self.sample_begin:
+ # suppress generating non-timestamp tokens at the beginning
+ logits[:, : self.tokenizer.timestamp_begin] = -np.inf
+
+ # apply the `max_initial_timestamp` option
+ if self.max_initial_timestamp_index is not None:
+ last_allowed = (
+ self.tokenizer.timestamp_begin + self.max_initial_timestamp_index
+ )
+ logits[:, last_allowed + 1 :] = -np.inf
+
+ # if sum of probability over timestamps is above any other token, sample timestamp
+ logprobs = F.log_softmax(logits.float(), dim=-1)
+ for k in range(tokens.shape[0]):
+ timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(
+ dim=-1
+ )
+ max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max()
+ if timestamp_logprob > max_text_token_logprob:
+ logits[k, : self.tokenizer.timestamp_begin] = -np.inf
+
+
+class DecodingTask:
+ inference: Inference
+ sequence_ranker: SequenceRanker
+ decoder: TokenDecoder
+ logit_filters: List[LogitFilter]
+
+ def __init__(self, model: "Whisper", options: DecodingOptions):
+ self.model = model
+
+ language = options.language or "en"
+ tokenizer = get_tokenizer(
+ model.is_multilingual, language=language, task=options.task
+ )
+ self.tokenizer: Tokenizer = tokenizer
+ self.options: DecodingOptions = self._verify_options(options)
+
+ self.n_group: int = options.beam_size or options.best_of or 1
+ self.n_ctx: int = model.dims.n_text_ctx
+ self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2
+
+ self.sot_sequence: Tuple[int] = tokenizer.sot_sequence
+ if self.options.without_timestamps:
+ self.sot_sequence = tokenizer.sot_sequence_including_notimestamps
+
+ self.initial_tokens: Tuple[int] = self._get_initial_tokens()
+ self.sample_begin: int = len(self.initial_tokens)
+ self.sot_index: int = self.initial_tokens.index(tokenizer.sot)
+
+ # inference: implements the forward pass through the decoder, including kv caching
+ self.inference = PyTorchInference(model, len(self.initial_tokens))
+
+ # sequence ranker: implements how to rank a group of sampled sequences
+ self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty)
+
+ # decoder: implements how to select the next tokens, given the autoregressive distribution
+ if options.beam_size is not None:
+ self.decoder = BeamSearchDecoder(
+ options.beam_size, tokenizer.eot, self.inference, options.patience
+ )
+ else:
+ self.decoder = GreedyDecoder(options.temperature, tokenizer.eot)
+
+ # logit filters: applies various rules to suppress or penalize certain tokens
+ self.logit_filters = []
+ if self.options.suppress_blank:
+ self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin))
+ if self.options.suppress_tokens:
+ self.logit_filters.append(SuppressTokens(self._get_suppress_tokens()))
+ if not options.without_timestamps:
+ precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds
+ max_initial_timestamp_index = None
+ if options.max_initial_timestamp:
+ max_initial_timestamp_index = round(
+ self.options.max_initial_timestamp / precision
+ )
+ self.logit_filters.append(
+ ApplyTimestampRules(
+ tokenizer, self.sample_begin, max_initial_timestamp_index
+ )
+ )
+
+ def _verify_options(self, options: DecodingOptions) -> DecodingOptions:
+ if options.beam_size is not None and options.best_of is not None:
+ raise ValueError("beam_size and best_of can't be given together")
+ if options.temperature == 0:
+ if options.best_of is not None:
+ raise ValueError("best_of with greedy sampling (T=0) is not compatible")
+ if options.patience is not None and options.beam_size is None:
+ raise ValueError("patience requires beam_size to be given")
+ if options.length_penalty is not None and not (
+ 0 <= options.length_penalty <= 1
+ ):
+ raise ValueError("length_penalty (alpha) should be a value between 0 and 1")
+
+ return options
+
+ def _get_initial_tokens(self) -> Tuple[int]:
+ tokens = list(self.sot_sequence)
+ prefix = self.options.prefix
+ prompt = self.options.prompt
+
+ if prefix:
+ prefix_tokens = (
+ self.tokenizer.encode(" " + prefix.strip())
+ if isinstance(prefix, str)
+ else prefix
+ )
+ if self.sample_len is not None:
+ max_prefix_len = self.n_ctx // 2 - self.sample_len
+ prefix_tokens = prefix_tokens[-max_prefix_len:]
+ tokens = tokens + prefix_tokens
+
+ if prompt:
+ prompt_tokens = (
+ self.tokenizer.encode(" " + prompt.strip())
+ if isinstance(prompt, str)
+ else prompt
+ )
+ tokens = (
+ [self.tokenizer.sot_prev]
+ + prompt_tokens[-(self.n_ctx // 2 - 1) :]
+ + tokens
+ )
+
+ return tuple(tokens)
+
+ def _get_suppress_tokens(self) -> Tuple[int]:
+ suppress_tokens = self.options.suppress_tokens
+
+ if isinstance(suppress_tokens, str):
+ suppress_tokens = [int(t) for t in suppress_tokens.split(",")]
+
+ if -1 in suppress_tokens:
+ suppress_tokens = [t for t in suppress_tokens if t >= 0]
+ suppress_tokens.extend(self.tokenizer.non_speech_tokens)
+ elif suppress_tokens is None or len(suppress_tokens) == 0:
+ suppress_tokens = [] # interpret empty string as an empty list
+ else:
+ assert isinstance(suppress_tokens, list), "suppress_tokens must be a list"
+
+ suppress_tokens.extend(
+ [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm]
+ )
+ if self.tokenizer.no_speech is not None:
+ # no-speech probability is collected separately
+ suppress_tokens.append(self.tokenizer.no_speech)
+
+ return tuple(sorted(set(suppress_tokens)))
+
+ def _get_audio_features(self, mel: Tensor):
+ if self.options.fp16:
+ mel = mel.half()
+
+ if mel.shape[-2:] == (
+ self.model.dims.n_audio_ctx,
+ self.model.dims.n_audio_state,
+ ):
+ # encoded audio features are given; skip audio encoding
+ audio_features = mel
+ else:
+ audio_features = self.model.encoder(mel)
+
+ if audio_features.dtype != (
+ torch.float16 if self.options.fp16 else torch.float32
+ ):
+ return TypeError(
+ f"audio_features has an incorrect dtype: {audio_features.dtype}"
+ )
+
+ return audio_features
+
+ def _detect_language(self, audio_features: Tensor, tokens: Tensor):
+ languages = [self.options.language] * audio_features.shape[0]
+ lang_probs = None
+
+ if self.options.language is None or self.options.task == "lang_id":
+ lang_tokens, lang_probs = self.model.detect_language(
+ audio_features, self.tokenizer
+ )
+ languages = [max(probs, key=probs.get) for probs in lang_probs]
+ if self.options.language is None:
+ tokens[:, self.sot_index + 1] = lang_tokens # write language tokens
+
+ return languages, lang_probs
+
+ def _main_loop(self, audio_features: Tensor, tokens: Tensor):
+ assert audio_features.shape[0] == tokens.shape[0]
+ n_batch = tokens.shape[0]
+ sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device)
+ no_speech_probs = [np.nan] * n_batch
+
+ try:
+ for i in range(self.sample_len):
+ logits = self.inference.logits(tokens, audio_features)
+
+ if (
+ i == 0 and self.tokenizer.no_speech is not None
+ ): # save no_speech_probs
+ probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1)
+ no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist()
+
+ # now we need to consider the logits at the last token only
+ logits = logits[:, -1]
+
+ # apply the logit filters, e.g. for suppressing or applying penalty to
+ for logit_filter in self.logit_filters:
+ logit_filter.apply(logits, tokens)
+
+ # expand the tokens tensor with the selected next tokens
+ tokens, completed = self.decoder.update(tokens, logits, sum_logprobs)
+
+ if completed or tokens.shape[-1] > self.n_ctx:
+ break
+ finally:
+ self.inference.cleanup_caching()
+
+ return tokens, sum_logprobs, no_speech_probs
+
+ @torch.no_grad()
+ def run(self, mel: Tensor) -> List[DecodingResult]:
+ self.decoder.reset()
+ tokenizer: Tokenizer = self.tokenizer
+ n_audio: int = mel.shape[0]
+
+ audio_features: Tensor = self._get_audio_features(mel) # encoder forward pass
+ tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1)
+
+ # detect language if requested, overwriting the language token
+ languages, language_probs = self._detect_language(audio_features, tokens)
+ if self.options.task == "lang_id":
+ return [
+ DecodingResult(
+ audio_features=features, language=language, language_probs=probs
+ )
+ for features, language, probs in zip(
+ audio_features, languages, language_probs
+ )
+ ]
+
+ # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling
+ audio_features = audio_features.repeat_interleave(self.n_group, dim=0)
+ tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device)
+
+ # call the main sampling loop
+ tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens)
+
+ # reshape the tensors to have (n_audio, n_group) as the first two dimensions
+ audio_features = audio_features[:: self.n_group]
+ no_speech_probs = no_speech_probs[:: self.n_group]
+ assert audio_features.shape[0] == len(no_speech_probs) == n_audio
+
+ tokens = tokens.reshape(n_audio, self.n_group, -1)
+ sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group)
+
+ # get the final candidates for each group, and slice between the first sampled token and EOT
+ tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs)
+ tokens: List[List[Tensor]] = [
+ [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s]
+ for s in tokens
+ ]
+
+ # select the top-ranked sample in each group
+ selected = self.sequence_ranker.rank(tokens, sum_logprobs)
+ tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)]
+ texts: List[str] = [tokenizer.decode(t).strip() for t in tokens]
+
+ sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)]
+ avg_logprobs: List[float] = [
+ lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)
+ ]
+
+ fields = (
+ texts,
+ languages,
+ tokens,
+ audio_features,
+ avg_logprobs,
+ no_speech_probs,
+ )
+ if len(set(map(len, fields))) != 1:
+ raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}")
+
+ return [
+ DecodingResult(
+ audio_features=features,
+ language=language,
+ tokens=tokens,
+ text=text,
+ avg_logprob=avg_logprob,
+ no_speech_prob=no_speech_prob,
+ temperature=self.options.temperature,
+ compression_ratio=compression_ratio(text),
+ )
+ for text, language, tokens, features, avg_logprob, no_speech_prob in zip(
+ *fields
+ )
+ ]
+
+
+@torch.no_grad()
+def decode(
+ model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()
+) -> Union[DecodingResult, List[DecodingResult]]:
+ """
+ Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s).
+
+ Parameters
+ ----------
+ model: Whisper
+ the Whisper model instance
+
+ mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000)
+ A tensor containing the Mel spectrogram(s)
+
+ options: DecodingOptions
+ A dataclass that contains all necessary options for decoding 30-second segments
+
+ Returns
+ -------
+ result: Union[DecodingResult, List[DecodingResult]]
+ The result(s) of decoding contained in `DecodingResult` dataclass instance(s)
+ """
+ single = mel.ndim == 2
+ if single:
+ mel = mel.unsqueeze(0)
+
+ result = DecodingTask(model, options).run(mel)
+
+ if single:
+ result = result[0]
+
+ return result
diff --git a/almeval/models/stepaudio/funasr_detach/models/whisper/utils/tokenizer.py b/almeval/models/stepaudio/funasr_detach/models/whisper/utils/tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..925598f845be0dea17bc7a9bc7b169348cfd3f70
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/whisper/utils/tokenizer.py
@@ -0,0 +1,351 @@
+import os
+from dataclasses import dataclass
+from functools import lru_cache
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+
+try:
+ from transformers import GPT2TokenizerFast
+except ImportError:
+ raise ImportError(
+ "transformers was not installed. Please install transformers first."
+ )
+
+LANGUAGES = {
+ "en": "english",
+ "zh": "chinese",
+ "de": "german",
+ "es": "spanish",
+ "ru": "russian",
+ "ko": "korean",
+ "fr": "french",
+ "ja": "japanese",
+ "pt": "portuguese",
+ "tr": "turkish",
+ "pl": "polish",
+ "ca": "catalan",
+ "nl": "dutch",
+ "ar": "arabic",
+ "sv": "swedish",
+ "it": "italian",
+ "id": "indonesian",
+ "hi": "hindi",
+ "fi": "finnish",
+ "vi": "vietnamese",
+ "he": "hebrew",
+ "uk": "ukrainian",
+ "el": "greek",
+ "ms": "malay",
+ "cs": "czech",
+ "ro": "romanian",
+ "da": "danish",
+ "hu": "hungarian",
+ "ta": "tamil",
+ "no": "norwegian",
+ "th": "thai",
+ "ur": "urdu",
+ "hr": "croatian",
+ "bg": "bulgarian",
+ "lt": "lithuanian",
+ "la": "latin",
+ "mi": "maori",
+ "ml": "malayalam",
+ "cy": "welsh",
+ "sk": "slovak",
+ "te": "telugu",
+ "fa": "persian",
+ "lv": "latvian",
+ "bn": "bengali",
+ "sr": "serbian",
+ "az": "azerbaijani",
+ "sl": "slovenian",
+ "kn": "kannada",
+ "et": "estonian",
+ "mk": "macedonian",
+ "br": "breton",
+ "eu": "basque",
+ "is": "icelandic",
+ "hy": "armenian",
+ "ne": "nepali",
+ "mn": "mongolian",
+ "bs": "bosnian",
+ "kk": "kazakh",
+ "sq": "albanian",
+ "sw": "swahili",
+ "gl": "galician",
+ "mr": "marathi",
+ "pa": "punjabi",
+ "si": "sinhala",
+ "km": "khmer",
+ "sn": "shona",
+ "yo": "yoruba",
+ "so": "somali",
+ "af": "afrikaans",
+ "oc": "occitan",
+ "ka": "georgian",
+ "be": "belarusian",
+ "tg": "tajik",
+ "sd": "sindhi",
+ "gu": "gujarati",
+ "am": "amharic",
+ "yi": "yiddish",
+ "lo": "lao",
+ "uz": "uzbek",
+ "fo": "faroese",
+ "ht": "haitian creole",
+ "ps": "pashto",
+ "tk": "turkmen",
+ "nn": "nynorsk",
+ "mt": "maltese",
+ "sa": "sanskrit",
+ "lb": "luxembourgish",
+ "my": "myanmar",
+ "bo": "tibetan",
+ "tl": "tagalog",
+ "mg": "malagasy",
+ "as": "assamese",
+ "tt": "tatar",
+ "haw": "hawaiian",
+ "ln": "lingala",
+ "ha": "hausa",
+ "ba": "bashkir",
+ "jw": "javanese",
+ "su": "sundanese",
+}
+
+# language code lookup by name, with a few language aliases
+TO_LANGUAGE_CODE = {
+ **{language: code for code, language in LANGUAGES.items()},
+ "burmese": "my",
+ "valencian": "ca",
+ "flemish": "nl",
+ "haitian": "ht",
+ "letzeburgesch": "lb",
+ "pushto": "ps",
+ "panjabi": "pa",
+ "moldavian": "ro",
+ "moldovan": "ro",
+ "sinhalese": "si",
+ "castilian": "es",
+}
+
+
+@dataclass(frozen=True)
+class Tokenizer:
+ """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens"""
+
+ tokenizer: "GPT2TokenizerFast"
+ language: Optional[str]
+ sot_sequence: Tuple[int]
+
+ def encode(self, text, **kwargs):
+ return self.tokenizer.encode(text, **kwargs)
+
+ def decode(
+ self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs
+ ):
+ return self.tokenizer.decode(token_ids, **kwargs)
+
+ def decode_with_timestamps(self, tokens) -> str:
+ """
+ Timestamp tokens are above the special tokens' id range and are ignored by `decode()`.
+ This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
+ """
+ outputs = [[]]
+ for token in tokens:
+ if token >= self.timestamp_begin:
+ timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>"
+ outputs.append(timestamp)
+ outputs.append([])
+ else:
+ outputs[-1].append(token)
+ outputs = [
+ s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs
+ ]
+ return "".join(outputs)
+
+ @property
+ @lru_cache()
+ def eot(self) -> int:
+ return self.tokenizer.eos_token_id
+
+ @property
+ @lru_cache()
+ def sot(self) -> int:
+ return self._get_single_token_id("<|startoftranscript|>")
+
+ @property
+ @lru_cache()
+ def sot_lm(self) -> int:
+ return self._get_single_token_id("<|startoflm|>")
+
+ @property
+ @lru_cache()
+ def sot_prev(self) -> int:
+ return self._get_single_token_id("<|startofprev|>")
+
+ @property
+ @lru_cache()
+ def no_speech(self) -> int:
+ return self._get_single_token_id("<|nospeech|>")
+
+ @property
+ @lru_cache()
+ def no_timestamps(self) -> int:
+ return self._get_single_token_id("<|notimestamps|>")
+
+ @property
+ @lru_cache()
+ def timestamp_begin(self) -> int:
+ return self.tokenizer.all_special_ids[-1] + 1
+
+ @property
+ @lru_cache()
+ def language_token(self) -> int:
+ """Returns the token id corresponding to the value of the `language` field"""
+ if self.language is None:
+ raise ValueError(f"This tokenizer does not have language token configured")
+
+ additional_tokens = dict(
+ zip(
+ self.tokenizer.additional_special_tokens,
+ self.tokenizer.additional_special_tokens_ids,
+ )
+ )
+ candidate = f"<|{self.language}|>"
+ if candidate in additional_tokens:
+ return additional_tokens[candidate]
+
+ raise KeyError(f"Language {self.language} not found in tokenizer.")
+
+ @property
+ @lru_cache()
+ def all_language_tokens(self) -> Tuple[int]:
+ result = []
+ for token, token_id in zip(
+ self.tokenizer.additional_special_tokens,
+ self.tokenizer.additional_special_tokens_ids,
+ ):
+ if token.strip("<|>") in LANGUAGES:
+ result.append(token_id)
+ return tuple(result)
+
+ @property
+ @lru_cache()
+ def all_language_codes(self) -> Tuple[str]:
+ return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens)
+
+ @property
+ @lru_cache()
+ def sot_sequence_including_notimestamps(self) -> Tuple[int]:
+ return tuple(list(self.sot_sequence) + [self.no_timestamps])
+
+ @property
+ @lru_cache()
+ def non_speech_tokens(self) -> Tuple[int]:
+ """
+ Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
+ annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
+
+ - ♪♪♪
+ - ( SPEAKING FOREIGN LANGUAGE )
+ - [DAVID] Hey there,
+
+ keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
+ """
+ symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』')
+ symbols += (
+ "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
+ )
+
+ # symbols that may be a single token or multiple tokens depending on the tokenizer.
+ # In case they're multiple tokens, suppress the first token, which is safe because:
+ # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
+ # in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
+ miscellaneous = set("♩♪♫♬♭♮♯")
+ assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
+
+ # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
+ result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]}
+ for symbol in symbols + list(miscellaneous):
+ for tokens in [
+ self.tokenizer.encode(symbol),
+ self.tokenizer.encode(" " + symbol),
+ ]:
+ if len(tokens) == 1 or symbol in miscellaneous:
+ result.add(tokens[0])
+
+ return tuple(sorted(result))
+
+ def _get_single_token_id(self, text) -> int:
+ tokens = self.tokenizer.encode(text)
+ assert len(tokens) == 1, f"{text} is not encoded as a single token"
+ return tokens[0]
+
+
+@lru_cache(maxsize=None)
+def build_tokenizer(name: str = "gpt2", resource_path: str = None):
+ os.environ["TOKENIZERS_PARALLELISM"] = "false"
+ if resource_path is not None:
+ path = os.path.join(resource_path, name)
+ else:
+ path = os.path.join(os.path.dirname(__file__), "assets", name)
+ tokenizer = GPT2TokenizerFast.from_pretrained(path)
+
+ specials = [
+ "<|startoftranscript|>",
+ *[f"<|{lang}|>" for lang in LANGUAGES.keys()],
+ "<|translate|>",
+ "<|transcribe|>",
+ "<|startoflm|>",
+ "<|startofprev|>",
+ "<|nospeech|>",
+ "<|notimestamps|>",
+ ]
+
+ tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
+ return tokenizer
+
+
+@lru_cache(maxsize=None)
+def get_tokenizer(
+ multilingual: bool,
+ *,
+ task: Optional[str] = None, # Literal["transcribe", "translate", None]
+ language: Optional[str] = None,
+) -> Tokenizer:
+ if language is not None:
+ language = language.lower()
+ if language not in LANGUAGES:
+ if language in TO_LANGUAGE_CODE:
+ language = TO_LANGUAGE_CODE[language]
+ else:
+ raise ValueError(f"Unsupported language: {language}")
+
+ if multilingual:
+ tokenizer_name = "multilingual"
+ task = task or "transcribe"
+ language = language or "en"
+ else:
+ tokenizer_name = "gpt2"
+ task = None
+ language = None
+
+ tokenizer = build_tokenizer(name=tokenizer_name)
+ all_special_ids: List[int] = tokenizer.all_special_ids
+ sot: int = all_special_ids[1]
+ translate: int = all_special_ids[-6]
+ transcribe: int = all_special_ids[-5]
+
+ langs = tuple(LANGUAGES.keys())
+ sot_sequence = [sot]
+ if language is not None:
+ sot_sequence.append(sot + 1 + langs.index(language))
+ if task is not None:
+ sot_sequence.append(transcribe if task == "transcribe" else translate)
+
+ return Tokenizer(
+ tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence)
+ )
diff --git a/almeval/models/stepaudio/funasr_detach/models/whisper/utils/transcribe.py b/almeval/models/stepaudio/funasr_detach/models/whisper/utils/transcribe.py
new file mode 100644
index 0000000000000000000000000000000000000000..c993b4ad5bbc42c919406c6c8b82e30287c7fe41
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/whisper/utils/transcribe.py
@@ -0,0 +1,494 @@
+import argparse
+import os
+import warnings
+from typing import Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+import torch
+import tqdm
+
+from funasr_detach.models.whisper.utils.audio import (
+ SAMPLE_RATE,
+ N_FRAMES,
+ HOP_LENGTH,
+ pad_or_trim,
+ log_mel_spectrogram,
+)
+from funasr_detach.models.whisper.utils.decoding import DecodingOptions, DecodingResult
+from funasr_detach.models.whisper.utils.tokenizer import (
+ LANGUAGES,
+ TO_LANGUAGE_CODE,
+ get_tokenizer,
+)
+from funasr_detach.models.whisper.utils.utils import (
+ exact_div,
+ format_timestamp,
+ make_safe,
+ optional_int,
+ optional_float,
+ str2bool,
+ get_writer,
+)
+
+if TYPE_CHECKING:
+ from .model import Whisper
+
+
+def transcribe(
+ model: "Whisper",
+ audio: Union[str, np.ndarray, torch.Tensor],
+ *,
+ verbose: Optional[bool] = None,
+ temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
+ compression_ratio_threshold: Optional[float] = 2.4,
+ logprob_threshold: Optional[float] = -1.0,
+ no_speech_threshold: Optional[float] = 0.6,
+ condition_on_previous_text: bool = True,
+ **decode_options,
+):
+ """
+ Transcribe an audio file using Whisper
+
+ Parameters
+ ----------
+ model: Whisper
+ The Whisper model instance
+
+ audio: Union[str, np.ndarray, torch.Tensor]
+ The path to the audio file to open, or the audio waveform
+
+ verbose: bool
+ Whether to display the text being decoded to the console. If True, displays all the details,
+ If False, displays minimal details. If None, does not display anything
+
+ temperature: Union[float, Tuple[float, ...]]
+ Temperature for sampling. It can be a tuple of temperatures, which will be successively used
+ upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.
+
+ compression_ratio_threshold: float
+ If the gzip compression ratio is above this value, treat as failed
+
+ logprob_threshold: float
+ If the average log probability over sampled tokens is below this value, treat as failed
+
+ no_speech_threshold: float
+ If the no_speech probability is higher than this value AND the average log probability
+ over sampled tokens is below `logprob_threshold`, consider the segment as silent
+
+ condition_on_previous_text: bool
+ if True, the previous output of the model is provided as a prompt for the next window;
+ disabling may make the text inconsistent across windows, but the model becomes less prone to
+ getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
+
+ decode_options: dict
+ Keyword arguments to construct `DecodingOptions` instances
+
+ Returns
+ -------
+ A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
+ the spoken language ("language"), which is detected when `decode_options["language"]` is None.
+ """
+ dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32
+ if model.device == torch.device("cpu"):
+ if torch.cuda.is_available():
+ warnings.warn("Performing inference on CPU when CUDA is available")
+ if dtype == torch.float16:
+ warnings.warn("FP16 is not supported on CPU; using FP32 instead")
+ dtype = torch.float32
+
+ if dtype == torch.float32:
+ decode_options["fp16"] = False
+
+ mel = log_mel_spectrogram(audio)
+
+ if decode_options.get("language", None) is None:
+ if not model.is_multilingual:
+ decode_options["language"] = "en"
+ else:
+ if verbose:
+ print(
+ "Detecting language using up to the first 30 seconds. Use `--language` to specify the language"
+ )
+ segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype)
+ _, probs = model.detect_language(segment)
+ decode_options["language"] = max(probs, key=probs.get)
+ if verbose is not None:
+ print(
+ f"Detected language: {LANGUAGES[decode_options['language']].title()}"
+ )
+
+ language = decode_options["language"]
+ task = decode_options.get("task", "transcribe")
+ tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task)
+
+ def decode_with_fallback(segment: torch.Tensor) -> DecodingResult:
+ temperatures = (
+ [temperature] if isinstance(temperature, (int, float)) else temperature
+ )
+ decode_result = None
+
+ for t in temperatures:
+ kwargs = {**decode_options}
+ if t > 0:
+ # disable beam_size and patience when t > 0
+ kwargs.pop("beam_size", None)
+ kwargs.pop("patience", None)
+ else:
+ # disable best_of when t == 0
+ kwargs.pop("best_of", None)
+
+ options = DecodingOptions(**kwargs, temperature=t)
+ decode_result = model.decode(segment, options)
+
+ needs_fallback = False
+ if (
+ compression_ratio_threshold is not None
+ and decode_result.compression_ratio > compression_ratio_threshold
+ ):
+ needs_fallback = True # too repetitive
+ if (
+ logprob_threshold is not None
+ and decode_result.avg_logprob < logprob_threshold
+ ):
+ needs_fallback = True # average log probability is too low
+
+ if not needs_fallback:
+ break
+
+ return decode_result
+
+ seek = 0
+ input_stride = exact_div(
+ N_FRAMES, model.dims.n_audio_ctx
+ ) # mel frames per output token: 2
+ time_precision = (
+ input_stride * HOP_LENGTH / SAMPLE_RATE
+ ) # time per output token: 0.02 (seconds)
+ all_tokens = []
+ all_segments = []
+ prompt_reset_since = 0
+
+ initial_prompt = decode_options.pop("initial_prompt", None) or []
+ if initial_prompt:
+ initial_prompt = tokenizer.encode(" " + initial_prompt.strip())
+ all_tokens.extend(initial_prompt)
+
+ def add_segment(
+ *, start: float, end: float, text_tokens: torch.Tensor, result: DecodingResult
+ ):
+ text = tokenizer.decode(
+ [token for token in text_tokens if token < tokenizer.eot]
+ )
+ if len(text.strip()) == 0: # skip empty text output
+ return
+
+ all_segments.append(
+ {
+ "id": len(all_segments),
+ "seek": seek,
+ "start": start,
+ "end": end,
+ "text": text,
+ "tokens": text_tokens.tolist(),
+ "temperature": result.temperature,
+ "avg_logprob": result.avg_logprob,
+ "compression_ratio": result.compression_ratio,
+ "no_speech_prob": result.no_speech_prob,
+ }
+ )
+ if verbose:
+ print(
+ make_safe(
+ f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}"
+ )
+ )
+
+ # show the progress bar when verbose is False (otherwise the transcribed text will be printed)
+ num_frames = mel.shape[-1]
+ previous_seek_value = seek
+
+ with tqdm.tqdm(
+ total=num_frames, unit="frames", disable=verbose is not False
+ ) as pbar:
+ while seek < num_frames:
+ timestamp_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
+ segment = pad_or_trim(mel[:, seek:], N_FRAMES).to(model.device).to(dtype)
+ segment_duration = segment.shape[-1] * HOP_LENGTH / SAMPLE_RATE
+
+ decode_options["prompt"] = all_tokens[prompt_reset_since:]
+ result: DecodingResult = decode_with_fallback(segment)
+ tokens = torch.tensor(result.tokens)
+
+ if no_speech_threshold is not None:
+ # no voice activity check
+ should_skip = result.no_speech_prob > no_speech_threshold
+ if (
+ logprob_threshold is not None
+ and result.avg_logprob > logprob_threshold
+ ):
+ # don't skip if the logprob is high enough, despite the no_speech_prob
+ should_skip = False
+
+ if should_skip:
+ seek += segment.shape[
+ -1
+ ] # fast-forward to the next segment boundary
+ continue
+
+ timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin)
+ consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[
+ 0
+ ].add_(1)
+ if (
+ len(consecutive) > 0
+ ): # if the output contains two consecutive timestamp tokens
+ last_slice = 0
+ for current_slice in consecutive:
+ sliced_tokens = tokens[last_slice:current_slice]
+ start_timestamp_position = (
+ sliced_tokens[0].item() - tokenizer.timestamp_begin
+ )
+ end_timestamp_position = (
+ sliced_tokens[-1].item() - tokenizer.timestamp_begin
+ )
+ add_segment(
+ start=timestamp_offset
+ + start_timestamp_position * time_precision,
+ end=timestamp_offset + end_timestamp_position * time_precision,
+ text_tokens=sliced_tokens[1:-1],
+ result=result,
+ )
+ last_slice = current_slice
+ last_timestamp_position = (
+ tokens[last_slice - 1].item() - tokenizer.timestamp_begin
+ )
+ seek += last_timestamp_position * input_stride
+ all_tokens.extend(tokens[: last_slice + 1].tolist())
+ else:
+ duration = segment_duration
+ timestamps = tokens[timestamp_tokens.nonzero().flatten()]
+ if (
+ len(timestamps) > 0
+ and timestamps[-1].item() != tokenizer.timestamp_begin
+ ):
+ # no consecutive timestamps but it has a timestamp; use the last one.
+ # single timestamp at the end means no speech after the last timestamp.
+ last_timestamp_position = (
+ timestamps[-1].item() - tokenizer.timestamp_begin
+ )
+ duration = last_timestamp_position * time_precision
+
+ add_segment(
+ start=timestamp_offset,
+ end=timestamp_offset + duration,
+ text_tokens=tokens,
+ result=result,
+ )
+
+ seek += segment.shape[-1]
+ all_tokens.extend(tokens.tolist())
+
+ if not condition_on_previous_text or result.temperature > 0.5:
+ # do not feed the prompt tokens if a high temperature was used
+ prompt_reset_since = len(all_tokens)
+
+ # update progress bar
+ pbar.update(min(num_frames, seek) - previous_seek_value)
+ previous_seek_value = seek
+
+ return dict(
+ text=tokenizer.decode(all_tokens[len(initial_prompt) :]),
+ segments=all_segments,
+ language=language,
+ )
+
+
+def cli():
+ from . import available_models
+
+ parser = argparse.ArgumentParser(
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter
+ )
+ parser.add_argument(
+ "audio", nargs="+", type=str, help="audio file(s) to transcribe"
+ )
+ parser.add_argument(
+ "--model",
+ default="small",
+ choices=available_models(),
+ help="name of the Whisper model to use",
+ )
+ parser.add_argument(
+ "--model_dir",
+ type=str,
+ default=None,
+ help="the path to save model files; uses ~/.cache/whisper by default",
+ )
+ parser.add_argument(
+ "--device",
+ default="cuda" if torch.cuda.is_available() else "cpu",
+ help="device to use for PyTorch inference",
+ )
+ parser.add_argument(
+ "--output_dir",
+ "-o",
+ type=str,
+ default=".",
+ help="directory to save the outputs",
+ )
+ parser.add_argument(
+ "--output_format",
+ "-f",
+ type=str,
+ default="all",
+ choices=["txt", "vtt", "srt", "tsv", "json", "all"],
+ help="format of the output file; if not specified, all available formats will be produced",
+ )
+ parser.add_argument(
+ "--verbose",
+ type=str2bool,
+ default=True,
+ help="whether to print out the progress and debug messages",
+ )
+
+ parser.add_argument(
+ "--task",
+ type=str,
+ default="transcribe",
+ choices=["transcribe", "translate"],
+ help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')",
+ )
+ parser.add_argument(
+ "--language",
+ type=str,
+ default=None,
+ choices=sorted(LANGUAGES.keys())
+ + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]),
+ help="language spoken in the audio, specify None to perform language detection",
+ )
+
+ parser.add_argument(
+ "--temperature", type=float, default=0, help="temperature to use for sampling"
+ )
+ parser.add_argument(
+ "--best_of",
+ type=optional_int,
+ default=5,
+ help="number of candidates when sampling with non-zero temperature",
+ )
+ parser.add_argument(
+ "--beam_size",
+ type=optional_int,
+ default=5,
+ help="number of beams in beam search, only applicable when temperature is zero",
+ )
+ parser.add_argument(
+ "--patience",
+ type=float,
+ default=None,
+ help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search",
+ )
+ parser.add_argument(
+ "--length_penalty",
+ type=float,
+ default=None,
+ help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default",
+ )
+
+ parser.add_argument(
+ "--suppress_tokens",
+ type=str,
+ default="-1",
+ help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations",
+ )
+ parser.add_argument(
+ "--initial_prompt",
+ type=str,
+ default=None,
+ help="optional text to provide as a prompt for the first window.",
+ )
+ parser.add_argument(
+ "--condition_on_previous_text",
+ type=str2bool,
+ default=True,
+ help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop",
+ )
+ parser.add_argument(
+ "--fp16",
+ type=str2bool,
+ default=True,
+ help="whether to perform inference in fp16; True by default",
+ )
+
+ parser.add_argument(
+ "--temperature_increment_on_fallback",
+ type=optional_float,
+ default=0.2,
+ help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below",
+ )
+ parser.add_argument(
+ "--compression_ratio_threshold",
+ type=optional_float,
+ default=2.4,
+ help="if the gzip compression ratio is higher than this value, treat the decoding as failed",
+ )
+ parser.add_argument(
+ "--logprob_threshold",
+ type=optional_float,
+ default=-1.0,
+ help="if the average log probability is lower than this value, treat the decoding as failed",
+ )
+ parser.add_argument(
+ "--no_speech_threshold",
+ type=optional_float,
+ default=0.6,
+ help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence",
+ )
+ parser.add_argument(
+ "--threads",
+ type=optional_int,
+ default=0,
+ help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS",
+ )
+
+ args = parser.parse_args().__dict__
+ model_name: str = args.pop("model")
+ model_dir: str = args.pop("model_dir")
+ output_dir: str = args.pop("output_dir")
+ output_format: str = args.pop("output_format")
+ device: str = args.pop("device")
+ os.makedirs(output_dir, exist_ok=True)
+
+ if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
+ if args["language"] is not None:
+ warnings.warn(
+ f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead."
+ )
+ args["language"] = "en"
+
+ temperature = args.pop("temperature")
+ temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback")
+ if temperature_increment_on_fallback is not None:
+ temperature = tuple(
+ np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback)
+ )
+ else:
+ temperature = [temperature]
+
+ threads = args.pop("threads")
+ if threads > 0:
+ torch.set_num_threads(threads)
+
+ from . import load_model
+
+ model = load_model(model_name, device=device, download_root=model_dir)
+
+ writer = get_writer(output_format, output_dir)
+
+ for audio_path in args.pop("audio"):
+ result = transcribe(model, audio_path, temperature=temperature, **args)
+ writer(result, audio_path)
+
+
+if __name__ == "__main__":
+ cli()
diff --git a/almeval/models/stepaudio/funasr_detach/models/whisper/utils/utils.py b/almeval/models/stepaudio/funasr_detach/models/whisper/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9c2c712217a44a1a343aa02710b64478e0cd9cb
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/models/whisper/utils/utils.py
@@ -0,0 +1,172 @@
+import json
+import os
+import sys
+import zlib
+from typing import Callable, TextIO
+
+system_encoding = sys.getdefaultencoding()
+
+if system_encoding != "utf-8":
+
+ def make_safe(string):
+ # replaces any character not representable using the system default encoding with an '?',
+ # avoiding UnicodeEncodeError (https://github.com/openai/whisper/discussions/729).
+ return string.encode(system_encoding, errors="replace").decode(system_encoding)
+
+else:
+
+ def make_safe(string):
+ # utf-8 can encode any Unicode code point, so no need to do the round-trip encoding
+ return string
+
+
+def exact_div(x, y):
+ assert x % y == 0
+ return x // y
+
+
+def str2bool(string):
+ str2val = {"True": True, "False": False}
+ if string in str2val:
+ return str2val[string]
+ else:
+ raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
+
+
+def optional_int(string):
+ return None if string == "None" else int(string)
+
+
+def optional_float(string):
+ return None if string == "None" else float(string)
+
+
+def compression_ratio(text) -> float:
+ text_bytes = text.encode("utf-8")
+ return len(text_bytes) / len(zlib.compress(text_bytes))
+
+
+def format_timestamp(
+ seconds: float, always_include_hours: bool = False, decimal_marker: str = "."
+):
+ assert seconds >= 0, "non-negative timestamp expected"
+ milliseconds = round(seconds * 1000.0)
+
+ hours = milliseconds // 3_600_000
+ milliseconds -= hours * 3_600_000
+
+ minutes = milliseconds // 60_000
+ milliseconds -= minutes * 60_000
+
+ seconds = milliseconds // 1_000
+ milliseconds -= seconds * 1_000
+
+ hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
+ return (
+ f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
+ )
+
+
+class ResultWriter:
+ extension: str
+
+ def __init__(self, output_dir: str):
+ self.output_dir = output_dir
+
+ def __call__(self, result: dict, audio_path: str):
+ audio_basename = os.path.basename(audio_path)
+ output_path = os.path.join(
+ self.output_dir, audio_basename + "." + self.extension
+ )
+
+ with open(output_path, "w", encoding="utf-8") as f:
+ self.write_result(result, file=f)
+
+ def write_result(self, result: dict, file: TextIO):
+ raise NotImplementedError
+
+
+class WriteTXT(ResultWriter):
+ extension: str = "txt"
+
+ def write_result(self, result: dict, file: TextIO):
+ for segment in result["segments"]:
+ print(segment["text"].strip(), file=file, flush=True)
+
+
+class WriteVTT(ResultWriter):
+ extension: str = "vtt"
+
+ def write_result(self, result: dict, file: TextIO):
+ print("WEBVTT\n", file=file)
+ for segment in result["segments"]:
+ print(
+ f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
+ f"{segment['text'].strip().replace('-->', '->')}\n",
+ file=file,
+ flush=True,
+ )
+
+
+class WriteSRT(ResultWriter):
+ extension: str = "srt"
+
+ def write_result(self, result: dict, file: TextIO):
+ for i, segment in enumerate(result["segments"], start=1):
+ # write srt lines
+ print(
+ f"{i}\n"
+ f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> "
+ f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n"
+ f"{segment['text'].strip().replace('-->', '->')}\n",
+ file=file,
+ flush=True,
+ )
+
+
+class WriteTSV(ResultWriter):
+ """
+ Write a transcript to a file in TSV (tab-separated values) format containing lines like:
+ \t\t
+
+ Using integer milliseconds as start and end times means there's no chance of interference from
+ an environment setting a language encoding that causes the decimal in a floating point number
+ to appear as a comma; also is faster and more efficient to parse & store, e.g., in C++.
+ """
+
+ extension: str = "tsv"
+
+ def write_result(self, result: dict, file: TextIO):
+ print("start", "end", "text", sep="\t", file=file)
+ for segment in result["segments"]:
+ print(round(1000 * segment["start"]), file=file, end="\t")
+ print(round(1000 * segment["end"]), file=file, end="\t")
+ print(segment["text"].strip().replace("\t", " "), file=file, flush=True)
+
+
+class WriteJSON(ResultWriter):
+ extension: str = "json"
+
+ def write_result(self, result: dict, file: TextIO):
+ json.dump(result, file)
+
+
+def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO], None]:
+ writers = {
+ "txt": WriteTXT,
+ "vtt": WriteVTT,
+ "srt": WriteSRT,
+ "tsv": WriteTSV,
+ "json": WriteJSON,
+ }
+
+ if output_format == "all":
+ all_writers = [writer(output_dir) for writer in writers.values()]
+
+ def write_all(result: dict, file: TextIO):
+ for writer in all_writers:
+ writer(result, file)
+
+ return write_all
+
+ return writers[output_format](output_dir)
diff --git a/almeval/models/stepaudio/funasr_detach/optimizers/__init__.py b/almeval/models/stepaudio/funasr_detach/optimizers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d6bd7e0b17a5eebc6945cdde8b86652d39a3156
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/optimizers/__init__.py
@@ -0,0 +1,17 @@
+import torch
+from funasr_detach.optimizers.fairseq_adam import FairseqAdam
+from funasr_detach.optimizers.sgd import SGD
+
+optim_classes = dict(
+ adam=torch.optim.Adam,
+ fairseq_adam=FairseqAdam,
+ adamw=torch.optim.AdamW,
+ sgd=SGD,
+ adadelta=torch.optim.Adadelta,
+ adagrad=torch.optim.Adagrad,
+ adamax=torch.optim.Adamax,
+ asgd=torch.optim.ASGD,
+ lbfgs=torch.optim.LBFGS,
+ rmsprop=torch.optim.RMSprop,
+ rprop=torch.optim.Rprop,
+)
diff --git a/almeval/models/stepaudio/funasr_detach/optimizers/fairseq_adam.py b/almeval/models/stepaudio/funasr_detach/optimizers/fairseq_adam.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d686aaf5d2706ed73f4e0baacdf98b52e949e06
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/optimizers/fairseq_adam.py
@@ -0,0 +1,152 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import math
+
+import torch
+import torch.optim
+
+
+class FairseqAdam(torch.optim.Optimizer):
+ r"""Implements Adam algorithm.
+
+ This implementation is modified from torch.optim.Adam based on:
+ `Fixed Weight Decay Regularization in Adam`
+ (see https://arxiv.org/abs/1711.05101)
+
+ It has been proposed in `Adam: A Method for Stochastic Optimization`_.
+
+ Args:
+ params (iterable): iterable of parameters to optimize or dicts defining
+ parameter groups
+ lr (float, optional): learning rate (default: 1e-3)
+ betas (Tuple[float, float], optional): coefficients used for computing
+ running averages of gradient and its square (default: (0.9, 0.999))
+ eps (float, optional): term added to the denominator to improve
+ numerical stability (default: 1e-8)
+ weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+ amsgrad (boolean, optional): whether to use the AMSGrad variant of this
+ algorithm from the paper `On the Convergence of Adam and Beyond`_
+
+ .. _Adam\: A Method for Stochastic Optimization:
+ https://arxiv.org/abs/1412.6980
+ .. _On the Convergence of Adam and Beyond:
+ https://openreview.net/forum?id=ryQu7f-RZ
+ """
+
+ def __init__(
+ self,
+ params,
+ lr=1e-3,
+ adam_betas=(0.9, 0.999),
+ adam_eps=1e-8,
+ weight_decay=0,
+ amsgrad=False,
+ ):
+ defaults = dict(
+ lr=lr,
+ betas=adam_betas,
+ eps=adam_eps,
+ weight_decay=weight_decay,
+ amsgrad=amsgrad,
+ )
+ super(FairseqAdam, self).__init__(params, defaults)
+ self.optimizer_lr = lr
+
+ @property
+ def supports_memory_efficient_fp16(self):
+ return True
+
+ @property
+ def supports_flat_params(self):
+ return True
+
+ def step(self, closure=None):
+ """Performs a single optimization step.
+
+ Args:
+ closure (callable, optional): A closure that reevaluates the model
+ and returns the loss.
+ """
+ loss = None
+ if closure is not None:
+ loss = closure()
+
+ for group in self.param_groups:
+ for p in group["params"]:
+ if p.grad is None:
+ continue
+ grad = p.grad.data
+ if grad.dtype in {torch.float16, torch.bfloat16}:
+ grad = grad.float()
+ if grad.is_sparse:
+ raise RuntimeError(
+ "Adam does not support sparse gradients, please consider SparseAdam instead"
+ )
+ amsgrad = group.get("amsgrad", False)
+
+ p_data_fp32 = p.data
+ if p.data.dtype in {torch.float16, torch.bfloat16}:
+ p_data_fp32 = p_data_fp32.float()
+
+ state = self.state[p]
+
+ # State initialization
+ if len(state) == 0:
+ state["step"] = 0
+ # Exponential moving average of gradient values
+ state["exp_avg"] = torch.zeros_like(p_data_fp32)
+ # Exponential moving average of squared gradient values
+ state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
+ if amsgrad:
+ # Maintains max of all exp. moving avg. of sq. grad. values
+ state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32)
+ else:
+ state["exp_avg"] = state["exp_avg"].to(p_data_fp32)
+ state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32)
+ if amsgrad:
+ state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to(
+ p_data_fp32
+ )
+
+ exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
+ if amsgrad:
+ max_exp_avg_sq = state["max_exp_avg_sq"]
+ beta1, beta2 = group["betas"]
+
+ state["step"] += 1
+
+ # Decay the first and second moment running average coefficient
+ exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+ exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+ if amsgrad:
+ # Maintains the maximum of all 2nd moment running avg. till now
+ torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
+ # Use the max. for normalizing running avg. of gradient
+ denom = max_exp_avg_sq.sqrt().add_(group["eps"])
+ else:
+ denom = exp_avg_sq.sqrt().add_(group["eps"])
+
+ bias_correction1 = 1 - beta1 ** state["step"]
+ bias_correction2 = 1 - beta2 ** state["step"]
+ step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1
+
+ if group["weight_decay"] != 0:
+ p_data_fp32.add_(
+ p_data_fp32, alpha=-group["weight_decay"] * group["lr"]
+ )
+
+ p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size)
+
+ if p.data.dtype in {torch.float16, torch.bfloat16}:
+ p.data.copy_(p_data_fp32)
+
+ return loss
+
+ def set_lr(self, lr):
+ """Set the learning rate."""
+ for param_group in self.param_groups:
+ param_group["lr"] = lr
diff --git a/almeval/models/stepaudio/funasr_detach/optimizers/sgd.py b/almeval/models/stepaudio/funasr_detach/optimizers/sgd.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb7a3df01ed428038361626ab1a8e40405bedb14
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/optimizers/sgd.py
@@ -0,0 +1,30 @@
+import torch
+
+
+class SGD(torch.optim.SGD):
+ """Thin inheritance of torch.optim.SGD to bind the required arguments, 'lr'
+
+ Note that
+ the arguments of the optimizer invoked by AbsTask.main()
+ must have default value except for 'param'.
+
+ I can't understand why only SGD.lr doesn't have the default value.
+ """
+
+ def __init__(
+ self,
+ params,
+ lr: float = 0.1,
+ momentum: float = 0.0,
+ dampening: float = 0.0,
+ weight_decay: float = 0.0,
+ nesterov: bool = False,
+ ):
+ super().__init__(
+ params,
+ lr=lr,
+ momentum=momentum,
+ dampening=dampening,
+ weight_decay=weight_decay,
+ nesterov=nesterov,
+ )
diff --git a/almeval/models/stepaudio/funasr_detach/register.py b/almeval/models/stepaudio/funasr_detach/register.py
new file mode 100644
index 0000000000000000000000000000000000000000..7280d2d2c63328111ef2203410544ba282103e37
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/register.py
@@ -0,0 +1,93 @@
+import logging
+import inspect
+from dataclasses import dataclass
+import re
+
+
+@dataclass
+class RegisterTables:
+ model_classes = {}
+ frontend_classes = {}
+ specaug_classes = {}
+ normalize_classes = {}
+ encoder_classes = {}
+ decoder_classes = {}
+ joint_network_classes = {}
+ predictor_classes = {}
+ stride_conv_classes = {}
+ tokenizer_classes = {}
+ batch_sampler_classes = {}
+ dataset_classes = {}
+ index_ds_classes = {}
+
+ def print(self, key=None):
+ print("\ntables: \n")
+ fields = vars(self)
+ for classes_key, classes_dict in fields.items():
+
+ flag = True
+ if key is not None:
+ flag = key in classes_key
+ if classes_key.endswith("_meta") and flag:
+ print(
+ f"----------- ** {classes_key.replace('_meta', '')} ** --------------"
+ )
+ headers = ["class name", "class location"]
+ metas = []
+ for register_key, meta in classes_dict.items():
+ metas.append(meta)
+ metas.sort(key=lambda x: x[0])
+ data = [headers] + metas
+ col_widths = [max(len(str(item)) for item in col) for col in zip(*data)]
+
+ for row in data:
+ print(
+ "| "
+ + " | ".join(
+ str(item).ljust(width)
+ for item, width in zip(row, col_widths)
+ )
+ + " |"
+ )
+ print("\n")
+
+ def register(self, register_tables_key: str, key=None):
+ def decorator(target_class):
+
+ if not hasattr(self, register_tables_key):
+ setattr(self, register_tables_key, {})
+ logging.info(
+ "new registry table has been added: {}".format(register_tables_key)
+ )
+
+ registry = getattr(self, register_tables_key)
+ registry_key = key if key is not None else target_class.__name__
+
+ # assert not registry_key in registry, "(key: {} / class: {}) has been registered already,in {}".format(
+ # registry_key, target_class, register_tables_key)
+
+ registry[registry_key] = target_class
+
+ # meta, headers = ["class name", "register name", "class location"]
+ register_tables_key_meta = register_tables_key + "_meta"
+ if not hasattr(self, register_tables_key_meta):
+ setattr(self, register_tables_key_meta, {})
+ registry_meta = getattr(self, register_tables_key_meta)
+ # doc = target_class.__doc__
+ class_file = inspect.getfile(target_class)
+ class_line = inspect.getsourcelines(target_class)[1]
+ pattern = r"^.+/funasr/"
+ class_file = re.sub(pattern, "funasr/", class_file)
+ meata_data = [f"{target_class.__name__}", f"{class_file}:{class_line}"]
+ # meata_data = [f"{target_class.__name__}", f"{registry_key}", f"{class_file}:{class_line}"]
+ registry_meta[registry_key] = meata_data
+ # print(f"Registering class: {class_file}:{class_line} - {target_class.__name__} as {registry_key}")
+ return target_class
+
+ return decorator
+
+
+tables = RegisterTables()
+
+# from funasr_detach.models.paraformer_streaming.model import ParaformerStreaming
+import funasr
diff --git a/almeval/models/stepaudio/funasr_detach/schedulers/__init__.py b/almeval/models/stepaudio/funasr_detach/schedulers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..25c2292ad062632f8df6f9aaf38c9f2b88d98cce
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/schedulers/__init__.py
@@ -0,0 +1,23 @@
+import torch
+import torch.multiprocessing
+import torch.nn
+import torch.optim
+
+from funasr_detach.schedulers.noam_lr import NoamLR
+from funasr_detach.schedulers.tri_stage_scheduler import TriStageLR
+from funasr_detach.schedulers.warmup_lr import WarmupLR
+
+scheduler_classes = dict(
+ ReduceLROnPlateau=torch.optim.lr_scheduler.ReduceLROnPlateau,
+ lambdalr=torch.optim.lr_scheduler.LambdaLR,
+ steplr=torch.optim.lr_scheduler.StepLR,
+ multisteplr=torch.optim.lr_scheduler.MultiStepLR,
+ exponentiallr=torch.optim.lr_scheduler.ExponentialLR,
+ CosineAnnealingLR=torch.optim.lr_scheduler.CosineAnnealingLR,
+ noamlr=NoamLR,
+ warmuplr=WarmupLR,
+ tri_stage=TriStageLR,
+ cycliclr=torch.optim.lr_scheduler.CyclicLR,
+ onecyclelr=torch.optim.lr_scheduler.OneCycleLR,
+ CosineAnnealingWarmRestarts=torch.optim.lr_scheduler.CosineAnnealingWarmRestarts,
+)
diff --git a/almeval/models/stepaudio/funasr_detach/schedulers/abs_scheduler.py b/almeval/models/stepaudio/funasr_detach/schedulers/abs_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..7395f259c3e563f560ea99080e00dac61967b421
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/schedulers/abs_scheduler.py
@@ -0,0 +1,84 @@
+from abc import ABC
+from abc import abstractmethod
+
+import torch.optim.lr_scheduler as L
+
+
+class AbsScheduler(ABC):
+ @abstractmethod
+ def step(self, epoch: int = None):
+ pass
+
+ @abstractmethod
+ def state_dict(self):
+ pass
+
+ @abstractmethod
+ def load_state_dict(self, state):
+ pass
+
+
+# If you need to define custom scheduler, please inherit these classes
+class AbsBatchStepScheduler(AbsScheduler):
+ @abstractmethod
+ def step(self, epoch: int = None):
+ pass
+
+ @abstractmethod
+ def state_dict(self):
+ pass
+
+ @abstractmethod
+ def load_state_dict(self, state):
+ pass
+
+
+class AbsEpochStepScheduler(AbsScheduler):
+ @abstractmethod
+ def step(self, epoch: int = None):
+ pass
+
+ @abstractmethod
+ def state_dict(self):
+ pass
+
+ @abstractmethod
+ def load_state_dict(self, state):
+ pass
+
+
+class AbsValEpochStepScheduler(AbsEpochStepScheduler):
+ @abstractmethod
+ def step(self, val, epoch: int = None):
+ pass
+
+ @abstractmethod
+ def state_dict(self):
+ pass
+
+ @abstractmethod
+ def load_state_dict(self, state):
+ pass
+
+
+# Create alias type to check the type
+# Note(kamo): Currently PyTorch doesn't provide the base class
+# to judge these classes.
+AbsValEpochStepScheduler.register(L.ReduceLROnPlateau)
+for s in [
+ L.ReduceLROnPlateau,
+ L.LambdaLR,
+ L.StepLR,
+ L.MultiStepLR,
+ L.MultiStepLR,
+ L.ExponentialLR,
+ L.CosineAnnealingLR,
+]:
+ AbsEpochStepScheduler.register(s)
+
+AbsBatchStepScheduler.register(L.CyclicLR)
+for s in [
+ L.OneCycleLR,
+ L.CosineAnnealingWarmRestarts,
+]:
+ AbsBatchStepScheduler.register(s)
diff --git a/almeval/models/stepaudio/funasr_detach/schedulers/noam_lr.py b/almeval/models/stepaudio/funasr_detach/schedulers/noam_lr.py
new file mode 100644
index 0000000000000000000000000000000000000000..975feb61ac2bb3d96cc999c6553a5ae5f870fc36
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/schedulers/noam_lr.py
@@ -0,0 +1,64 @@
+"""Noam learning rate scheduler module."""
+
+from typing import Union
+import warnings
+
+import torch
+from torch.optim.lr_scheduler import _LRScheduler
+
+from funasr_detach.schedulers.abs_scheduler import AbsBatchStepScheduler
+
+
+class NoamLR(_LRScheduler, AbsBatchStepScheduler):
+ """The LR scheduler proposed by Noam
+
+ Ref:
+ "Attention Is All You Need", https://arxiv.org/pdf/1706.03762.pdf
+
+ FIXME(kamo): PyTorch doesn't provide _LRScheduler as public class,
+ thus the behaviour isn't guaranteed at forward PyTorch version.
+
+ NOTE(kamo): The "model_size" in original implementation is derived from
+ the model, but in this implementation, this parameter is a constant value.
+ You need to change it if the model is changed.
+
+ """
+
+ def __init__(
+ self,
+ optimizer: torch.optim.Optimizer,
+ model_size: Union[int, float] = 320,
+ warmup_steps: Union[int, float] = 25000,
+ last_epoch: int = -1,
+ ):
+ self.model_size = model_size
+ self.warmup_steps = warmup_steps
+
+ lr = list(optimizer.param_groups)[0]["lr"]
+ new_lr = self.lr_for_WarmupLR(lr)
+ warnings.warn(
+ f"NoamLR is deprecated. "
+ f"Use WarmupLR(warmup_steps={warmup_steps}) with Optimizer(lr={new_lr})",
+ )
+
+ # __init__() must be invoked before setting field
+ # because step() is also invoked in __init__()
+ super().__init__(optimizer, last_epoch)
+
+ def lr_for_WarmupLR(self, lr: float) -> float:
+ return lr / self.model_size**0.5 / self.warmup_steps**0.5
+
+ def __repr__(self):
+ return (
+ f"{self.__class__.__name__}(model_size={self.model_size}, "
+ f"warmup_steps={self.warmup_steps})"
+ )
+
+ def get_lr(self):
+ step_num = self.last_epoch + 1
+ return [
+ lr
+ * self.model_size**-0.5
+ * min(step_num**-0.5, step_num * self.warmup_steps**-1.5)
+ for lr in self.base_lrs
+ ]
diff --git a/almeval/models/stepaudio/funasr_detach/schedulers/tri_stage_scheduler.py b/almeval/models/stepaudio/funasr_detach/schedulers/tri_stage_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..86c8a6a82e9d42b5c36f5f296ce2e060f4d6bf20
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/schedulers/tri_stage_scheduler.py
@@ -0,0 +1,106 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+from typing import Optional, List
+
+import torch
+from torch.optim.lr_scheduler import _LRScheduler
+
+from funasr_detach.schedulers.abs_scheduler import AbsBatchStepScheduler
+
+
+class TriStageLR(_LRScheduler, AbsBatchStepScheduler):
+ def __init__(
+ self,
+ optimizer: torch.optim.Optimizer,
+ last_epoch: int = -1,
+ phase_ratio: Optional[List[float]] = None,
+ init_lr_scale: float = 0.01,
+ final_lr_scale: float = 0.01,
+ ):
+ self.optimizer = optimizer
+ self.last_epoch = last_epoch
+ self.phase_ratio = phase_ratio
+ self.init_lr_scale = init_lr_scale
+ self.final_lr_scale = final_lr_scale
+ self.optimizer_lr = self.optimizer.defaults["lr"]
+
+ def init_tri_stage_scheudler(self, max_update):
+ self.max_update = max_update
+ self.peak_lr = self.optimizer_lr
+ self.init_lr = self.init_lr_scale * self.optimizer_lr
+ self.final_lr = self.final_lr_scale * self.optimizer_lr
+
+ assert self.max_update > 0
+ assert sum(self.phase_ratio) == 1, "phase ratios must add up to 1"
+ assert len(self.phase_ratio) == 3
+ self.warmup_steps = int(self.max_update * self.phase_ratio[0])
+ self.hold_steps = int(self.max_update * self.phase_ratio[1])
+ self.decay_steps = int(self.max_update * self.phase_ratio[2])
+
+ self.warmup_rate = (
+ (self.peak_lr - self.init_lr) / self.warmup_steps
+ if self.warmup_steps != 0
+ else 0
+ )
+ self.decay_factor = -math.log(self.final_lr_scale) / self.decay_steps
+
+ # initial learning rate
+ self.lr = self.init_lr
+
+ # __init__() must be invoked before setting field
+ # because step() is also invoked in __init__()
+ self.set_optimizer_lr(self.lr)
+ super().__init__(self.optimizer, self.last_epoch)
+
+ def _decide_stage(self, update_step):
+ """
+ return stage, and the corresponding steps within the current stage
+ """
+ if update_step < self.warmup_steps:
+ # warmup state
+ return 0, update_step
+
+ offset = self.warmup_steps
+
+ if update_step < offset + self.hold_steps:
+ # hold stage
+ return 1, update_step - offset
+
+ offset += self.hold_steps
+
+ if update_step <= offset + self.decay_steps:
+ # decay stage
+ return 2, update_step - offset
+
+ offset += self.decay_steps
+
+ # still here ? constant lr stage
+ return 3, update_step - offset
+
+ def step_update(self, num_updates):
+ """Update the learning rate after each update."""
+ stage, steps_in_stage = self._decide_stage(num_updates)
+ if stage == 0:
+ self.lr = self.init_lr + self.warmup_rate * steps_in_stage
+ elif stage == 1:
+ self.lr = self.peak_lr
+ elif stage == 2:
+ self.lr = self.peak_lr * math.exp(-self.decay_factor * steps_in_stage)
+ elif stage == 3:
+ self.lr = self.final_lr
+ else:
+ raise ValueError("Undefined stage")
+ self.set_optimizer_lr(self.lr)
+
+ def set_optimizer_lr(self, lr):
+ for param_group in self.optimizer.param_groups:
+ param_group["lr"] = lr
+
+ def get_lr(self):
+ step_num = self.last_epoch + 1
+ self.step_update(step_num)
+ return [self.lr]
diff --git a/almeval/models/stepaudio/funasr_detach/schedulers/warmup_lr.py b/almeval/models/stepaudio/funasr_detach/schedulers/warmup_lr.py
new file mode 100644
index 0000000000000000000000000000000000000000..361a5be12e9d3586117fe9da6a9ea456963e3b88
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/schedulers/warmup_lr.py
@@ -0,0 +1,49 @@
+"""Warm up learning rate scheduler module."""
+
+from typing import Union
+
+import torch
+from torch.optim.lr_scheduler import _LRScheduler
+
+from funasr_detach.schedulers.abs_scheduler import AbsBatchStepScheduler
+
+
+class WarmupLR(_LRScheduler, AbsBatchStepScheduler):
+ """The WarmupLR scheduler
+
+ This scheduler is almost same as NoamLR Scheduler except for following difference:
+
+ NoamLR:
+ lr = optimizer.lr * model_size ** -0.5
+ * min(step ** -0.5, step * warmup_step ** -1.5)
+ WarmupLR:
+ lr = optimizer.lr * warmup_step ** 0.5
+ * min(step ** -0.5, step * warmup_step ** -1.5)
+
+ Note that the maximum lr equals to optimizer.lr in this scheduler.
+
+ """
+
+ def __init__(
+ self,
+ optimizer: torch.optim.Optimizer,
+ warmup_steps: Union[int, float] = 25000,
+ last_epoch: int = -1,
+ ):
+ self.warmup_steps = warmup_steps
+
+ # __init__() must be invoked before setting field
+ # because step() is also invoked in __init__()
+ super().__init__(optimizer, last_epoch)
+
+ def __repr__(self):
+ return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps})"
+
+ def get_lr(self):
+ step_num = self.last_epoch + 1
+ return [
+ lr
+ * self.warmup_steps**0.5
+ * min(step_num**-0.5, step_num * self.warmup_steps**-1.5)
+ for lr in self.base_lrs
+ ]
diff --git a/almeval/models/stepaudio/funasr_detach/train_utils/__init__.py b/almeval/models/stepaudio/funasr_detach/train_utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/almeval/models/stepaudio/funasr_detach/train_utils/add_gradient_noise.py b/almeval/models/stepaudio/funasr_detach/train_utils/add_gradient_noise.py
new file mode 100644
index 0000000000000000000000000000000000000000..654e928ec003b9592120451230c1e2253b2b09c0
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/train_utils/add_gradient_noise.py
@@ -0,0 +1,31 @@
+import torch
+
+
+def add_gradient_noise(
+ model: torch.nn.Module,
+ iteration: int,
+ duration: float = 100,
+ eta: float = 1.0,
+ scale_factor: float = 0.55,
+):
+ """Adds noise from a standard normal distribution to the gradients.
+
+ The standard deviation (`sigma`) is controlled
+ by the three hyper-parameters below.
+ `sigma` goes to zero (no noise) with more iterations.
+
+ Args:
+ model: Model.
+ iteration: Number of iterations.
+ duration: {100, 1000}: Number of durations to control
+ the interval of the `sigma` change.
+ eta: {0.01, 0.3, 1.0}: The magnitude of `sigma`.
+ scale_factor: {0.55}: The scale of `sigma`.
+ """
+ interval = (iteration // duration) + 1
+ sigma = eta / interval**scale_factor
+ for param in model.parameters():
+ if param.grad is not None:
+ _shape = param.grad.size()
+ noise = sigma * torch.randn(_shape).to(param.device)
+ param.grad += noise
diff --git a/almeval/models/stepaudio/funasr_detach/train_utils/average_nbest_models.py b/almeval/models/stepaudio/funasr_detach/train_utils/average_nbest_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfccd84116af84f1718e249bc84e82d122ae06f9
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/train_utils/average_nbest_models.py
@@ -0,0 +1,187 @@
+import logging
+from pathlib import Path
+from typing import Optional
+from typing import Sequence
+from typing import Union
+import warnings
+import os
+from io import BytesIO
+
+import torch
+from typing import Collection
+import os
+import torch
+import re
+from collections import OrderedDict
+from functools import cmp_to_key
+
+
+# @torch.no_grad()
+# def average_nbest_models(
+# output_dir: Path,
+# best_model_criterion: Sequence[Sequence[str]],
+# nbest: Union[Collection[int], int],
+# suffix: Optional[str] = None,
+# oss_bucket=None,
+# pai_output_dir=None,
+# ) -> None:
+# """Generate averaged model from n-best models
+#
+# Args:
+# output_dir: The directory contains the model file for each epoch
+# reporter: Reporter instance
+# best_model_criterion: Give criterions to decide the best model.
+# e.g. [("valid", "loss", "min"), ("train", "acc", "max")]
+# nbest: Number of best model files to be averaged
+# suffix: A suffix added to the averaged model file name
+# """
+# if isinstance(nbest, int):
+# nbests = [nbest]
+# else:
+# nbests = list(nbest)
+# if len(nbests) == 0:
+# warnings.warn("At least 1 nbest values are required")
+# nbests = [1]
+# if suffix is not None:
+# suffix = suffix + "."
+# else:
+# suffix = ""
+#
+# # 1. Get nbests: List[Tuple[str, str, List[Tuple[epoch, value]]]]
+# nbest_epochs = [
+# (ph, k, reporter.sort_epochs_and_values(ph, k, m)[: max(nbests)])
+# for ph, k, m in best_model_criterion
+# if reporter.has(ph, k)
+# ]
+#
+# _loaded = {}
+# for ph, cr, epoch_and_values in nbest_epochs:
+# _nbests = [i for i in nbests if i <= len(epoch_and_values)]
+# if len(_nbests) == 0:
+# _nbests = [1]
+#
+# for n in _nbests:
+# if n == 0:
+# continue
+# elif n == 1:
+# # The averaged model is same as the best model
+# e, _ = epoch_and_values[0]
+# op = output_dir / f"{e}epoch.pb"
+# sym_op = output_dir / f"{ph}.{cr}.ave_1best.{suffix}pb"
+# if sym_op.is_symlink() or sym_op.exists():
+# sym_op.unlink()
+# sym_op.symlink_to(op.name)
+# else:
+# op = output_dir / f"{ph}.{cr}.ave_{n}best.{suffix}pb"
+# logging.info(
+# f"Averaging {n}best models: " f'criterion="{ph}.{cr}": {op}'
+# )
+#
+# avg = None
+# # 2.a. Averaging model
+# for e, _ in epoch_and_values[:n]:
+# if e not in _loaded:
+# if oss_bucket is None:
+# _loaded[e] = torch.load(
+# output_dir / f"{e}epoch.pb",
+# map_location="cpu",
+# )
+# else:
+# buffer = BytesIO(
+# oss_bucket.get_object(os.path.join(pai_output_dir, f"{e}epoch.pb")).read())
+# _loaded[e] = torch.load(buffer)
+# states = _loaded[e]
+#
+# if avg is None:
+# avg = states
+# else:
+# # Accumulated
+# for k in avg:
+# avg[k] = avg[k] + states[k]
+# for k in avg:
+# if str(avg[k].dtype).startswith("torch.int"):
+# # For int type, not averaged, but only accumulated.
+# # e.g. BatchNorm.num_batches_tracked
+# # (If there are any cases that requires averaging
+# # or the other reducing method, e.g. max/min, for integer type,
+# # please report.)
+# pass
+# else:
+# avg[k] = avg[k] / n
+#
+# # 2.b. Save the ave model and create a symlink
+# if oss_bucket is None:
+# torch.save(avg, op)
+# else:
+# buffer = BytesIO()
+# torch.save(avg, buffer)
+# oss_bucket.put_object(os.path.join(pai_output_dir, f"{ph}.{cr}.ave_{n}best.{suffix}pb"),
+# buffer.getvalue())
+#
+# # 3. *.*.ave.pb is a symlink to the max ave model
+# if oss_bucket is None:
+# op = output_dir / f"{ph}.{cr}.ave_{max(_nbests)}best.{suffix}pb"
+# sym_op = output_dir / f"{ph}.{cr}.ave.{suffix}pb"
+# if sym_op.is_symlink() or sym_op.exists():
+# sym_op.unlink()
+# sym_op.symlink_to(op.name)
+
+
+def _get_checkpoint_paths(output_dir: str, last_n: int = 5):
+ """
+ Get the paths of the last 'last_n' checkpoints by parsing filenames
+ in the output directory.
+ """
+ # List all files in the output directory
+ files = os.listdir(output_dir)
+ # Filter out checkpoint files and extract epoch numbers
+ checkpoint_files = [f for f in files if f.startswith("model.pt.e")]
+ # Sort files by epoch number in descending order
+ checkpoint_files.sort(
+ key=lambda x: int(re.search(r"(\d+)", x).group()), reverse=True
+ )
+ # Get the last 'last_n' checkpoint paths
+ checkpoint_paths = [os.path.join(output_dir, f) for f in checkpoint_files[:last_n]]
+ return checkpoint_paths
+
+
+@torch.no_grad()
+def average_checkpoints(output_dir: str, last_n: int = 5):
+ """
+ Average the last 'last_n' checkpoints' model state_dicts.
+ If a tensor is of type torch.int, perform sum instead of average.
+ """
+ checkpoint_paths = _get_checkpoint_paths(output_dir, last_n)
+ state_dicts = []
+
+ # Load state_dicts from checkpoints
+ for path in checkpoint_paths:
+ if os.path.isfile(path):
+ state_dicts.append(torch.load(path, map_location="cpu")["state_dict"])
+ else:
+ print(f"Checkpoint file {path} not found.")
+ continue
+
+ # Check if we have any state_dicts to average
+ if not state_dicts:
+ raise RuntimeError("No checkpoints found for averaging.")
+
+ # Average or sum weights
+ avg_state_dict = OrderedDict()
+ for key in state_dicts[0].keys():
+ tensors = [state_dict[key].cpu() for state_dict in state_dicts]
+ # Check the type of the tensor
+ if str(tensors[0].dtype).startswith("torch.int"):
+ # Perform sum for integer tensors
+ summed_tensor = sum(tensors)
+ avg_state_dict[key] = summed_tensor
+ else:
+ # Perform average for other types of tensors
+ stacked_tensors = torch.stack(tensors)
+ avg_state_dict[key] = torch.mean(stacked_tensors, dim=0)
+
+ torch.save(
+ {"state_dict": avg_state_dict},
+ os.path.join(output_dir, f"model.pt.avg{last_n}"),
+ )
+ return avg_state_dict
diff --git a/almeval/models/stepaudio/funasr_detach/train_utils/device_funcs.py b/almeval/models/stepaudio/funasr_detach/train_utils/device_funcs.py
new file mode 100644
index 0000000000000000000000000000000000000000..7919e7d923200e0deb67bc1a8f83ad56c599d274
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/train_utils/device_funcs.py
@@ -0,0 +1,71 @@
+import dataclasses
+import warnings
+
+import numpy as np
+import torch
+
+
+def to_device(data, device=None, dtype=None, non_blocking=False, copy=False):
+ """Change the device of object recursively"""
+ if isinstance(data, dict):
+ return {
+ k: to_device(v, device, dtype, non_blocking, copy) for k, v in data.items()
+ }
+ elif dataclasses.is_dataclass(data) and not isinstance(data, type):
+ return type(data)(
+ *[
+ to_device(v, device, dtype, non_blocking, copy)
+ for v in dataclasses.astuple(data)
+ ]
+ )
+ # maybe namedtuple. I don't know the correct way to judge namedtuple.
+ elif isinstance(data, tuple) and type(data) is not tuple:
+ return type(data)(
+ *[to_device(o, device, dtype, non_blocking, copy) for o in data]
+ )
+ elif isinstance(data, (list, tuple)):
+ return type(data)(to_device(v, device, dtype, non_blocking, copy) for v in data)
+ elif isinstance(data, np.ndarray):
+ return to_device(torch.from_numpy(data), device, dtype, non_blocking, copy)
+ elif isinstance(data, torch.Tensor):
+ return data.to(device, dtype, non_blocking, copy)
+ else:
+ return data
+
+
+def force_gatherable(data, device):
+ """Change object to gatherable in torch.nn.DataParallel recursively
+
+ The difference from to_device() is changing to torch.Tensor if float or int
+ value is found.
+
+ The restriction to the returned value in DataParallel:
+ The object must be
+ - torch.cuda.Tensor
+ - 1 or more dimension. 0-dimension-tensor sends warning.
+ or a list, tuple, dict.
+
+ """
+ if isinstance(data, dict):
+ return {k: force_gatherable(v, device) for k, v in data.items()}
+ # DataParallel can't handle NamedTuple well
+ elif isinstance(data, tuple) and type(data) is not tuple:
+ return type(data)(*[force_gatherable(o, device) for o in data])
+ elif isinstance(data, (list, tuple, set)):
+ return type(data)(force_gatherable(v, device) for v in data)
+ elif isinstance(data, np.ndarray):
+ return force_gatherable(torch.from_numpy(data), device)
+ elif isinstance(data, torch.Tensor):
+ if data.dim() == 0:
+ # To 1-dim array
+ data = data[None]
+ return data.to(device)
+ elif isinstance(data, float):
+ return torch.tensor([data], dtype=torch.float, device=device)
+ elif isinstance(data, int):
+ return torch.tensor([data], dtype=torch.long, device=device)
+ elif data is None:
+ return None
+ else:
+ warnings.warn(f"{type(data)} may not be gatherable by DataParallel")
+ return data
diff --git a/almeval/models/stepaudio/funasr_detach/train_utils/forward_adaptor.py b/almeval/models/stepaudio/funasr_detach/train_utils/forward_adaptor.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb6da2bb189690e85bc9192b5bd1958854a1353c
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/train_utils/forward_adaptor.py
@@ -0,0 +1,31 @@
+import torch
+
+
+class ForwardAdaptor(torch.nn.Module):
+ """Wrapped module to parallelize specified method
+
+ torch.nn.DataParallel parallelizes only "forward()"
+ and, maybe, the method having the other name can't be applied
+ except for wrapping the module just like this class.
+
+ Examples:
+ >>> class A(torch.nn.Module):
+ ... def foo(self, x):
+ ... ...
+ >>> model = A()
+ >>> model = ForwardAdaptor(model, "foo")
+ >>> model = torch.nn.DataParallel(model, device_ids=[0, 1])
+ >>> x = torch.randn(2, 10)
+ >>> model(x)
+ """
+
+ def __init__(self, module: torch.nn.Module, name: str):
+ super().__init__()
+ self.module = module
+ self.name = name
+ if not hasattr(module, name):
+ raise ValueError(f"{module} doesn't have {name}")
+
+ def forward(self, *args, **kwargs):
+ func = getattr(self.module, self.name)
+ return func(*args, **kwargs)
diff --git a/almeval/models/stepaudio/funasr_detach/train_utils/initialize.py b/almeval/models/stepaudio/funasr_detach/train_utils/initialize.py
new file mode 100644
index 0000000000000000000000000000000000000000..667585a27412838823da5b63626097bdf1706f67
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/train_utils/initialize.py
@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+
+"""Initialize modules for espnet2 neural networks."""
+
+import math
+import torch
+
+
+def initialize(model: torch.nn.Module, init: str):
+ """Initialize weights of a neural network module.
+
+ Parameters are initialized using the given method or distribution.
+
+ Custom initialization routines can be implemented into submodules
+ as function `espnet_initialization_fn` within the custom module.
+
+ Args:
+ model: Target.
+ init: Method of initialization.
+ """
+
+ # weight init
+ for p in model.parameters():
+ if p.dim() > 1:
+ if init == "xavier_uniform":
+ torch.nn.init.xavier_uniform_(p.data)
+ elif init == "xavier_normal":
+ torch.nn.init.xavier_normal_(p.data)
+ elif init == "kaiming_uniform":
+ torch.nn.init.kaiming_uniform_(p.data, nonlinearity="relu")
+ elif init == "kaiming_normal":
+ torch.nn.init.kaiming_normal_(p.data, nonlinearity="relu")
+ else:
+ raise ValueError("Unknown initialization: " + init)
+ # bias init
+ for p in model.parameters():
+ if p.dim() == 1:
+ p.data.zero_()
+
+ # reset some modules with default init
+ for m in model.modules():
+ if isinstance(m, (torch.nn.Embedding, torch.nn.LayerNorm, torch.nn.GroupNorm)):
+ m.reset_parameters()
+ if hasattr(m, "espnet_initialization_fn"):
+ m.espnet_initialization_fn()
+
+ # TODO(xkc): Hacking s3prl_frontend and wav2vec2encoder initialization
+ if getattr(model, "encoder", None) and getattr(
+ model.encoder, "reload_pretrained_parameters", None
+ ):
+ model.encoder.reload_pretrained_parameters()
+ if getattr(model, "frontend", None) and getattr(
+ model.frontend, "reload_pretrained_parameters", None
+ ):
+ model.frontend.reload_pretrained_parameters()
diff --git a/almeval/models/stepaudio/funasr_detach/train_utils/load_pretrained_model.py b/almeval/models/stepaudio/funasr_detach/train_utils/load_pretrained_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c7eee4e071b162c84eb22e91abc660ca6a3929b
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/train_utils/load_pretrained_model.py
@@ -0,0 +1,172 @@
+from typing import Any
+from typing import Dict
+from typing import Union
+from io import BytesIO
+
+import logging
+import torch
+import torch.nn
+import torch.optim
+
+
+def filter_state_dict(
+ dst_state: Dict[str, Union[float, torch.Tensor]],
+ src_state: Dict[str, Union[float, torch.Tensor]],
+):
+ """Filter name, size mismatch instances between dicts.
+
+ Args:
+ dst_state: reference state dict for filtering
+ src_state: target state dict for filtering
+
+ """
+ match_state = {}
+ for key, value in src_state.items():
+ if key in dst_state and (dst_state[key].size() == src_state[key].size()):
+ match_state[key] = value
+ else:
+ if key not in dst_state:
+ logging.warning(
+ f"Filter out {key} from pretrained dict"
+ + " because of name not found in target dict"
+ )
+ else:
+ logging.warning(
+ f"Filter out {key} from pretrained dict"
+ + " because of size mismatch"
+ + f"({dst_state[key].size()}-{src_state[key].size()})"
+ )
+ return match_state
+
+
+def assigment_scope_map(dst_state: dict, src_state: dict, scope_map: str = None):
+ """Compute the union of the current variables and checkpoint variables."""
+ import collections
+ import re
+
+ # current model variables
+ name_to_variable = collections.OrderedDict()
+ for name, var in dst_state.items():
+ name_to_variable[name] = var
+
+ scope_map_num = 0
+ if scope_map is not None:
+ scope_map = scope_map.split(",")
+ scope_map_num = len(scope_map) // 2
+ for scope_map_idx in range(scope_map_num):
+ scope_map_id = scope_map_idx * 2
+ logging.info(
+ "assignment_map from scope {} to {}".format(
+ scope_map[scope_map_id], scope_map[scope_map_id + 1]
+ )
+ )
+
+ assignment_map = {}
+ for name, var in src_state.items():
+
+ if scope_map:
+ for scope_map_idx in range(scope_map_num):
+ scope_map_id = scope_map_idx * 2
+ try:
+ idx = name.index(scope_map[scope_map_id])
+ new_name = (
+ scope_map[scope_map_id + 1]
+ + name[idx + len(scope_map[scope_map_id]) :]
+ )
+ if new_name in name_to_variable:
+ assignment_map[name] = var
+ except:
+ continue
+ else:
+ if name in name_to_variable:
+ assignment_map[name] = var
+
+ return assignment_map
+
+
+def load_pretrained_model(
+ path: str,
+ model: torch.nn.Module,
+ ignore_init_mismatch: bool,
+ map_location: str = "cpu",
+ oss_bucket=None,
+ scope_map=None,
+ excludes=None,
+):
+ """Load a model state and set it to the model.
+
+ Args:
+ init_param: :::
+
+ Examples:
+
+ """
+
+ obj = model
+ dst_state = obj.state_dict()
+ # import pdb;
+ # pdb.set_trace()
+ print(f"ckpt: {path}")
+ if oss_bucket is None:
+ src_state = torch.load(path, map_location=map_location)
+ else:
+ buffer = BytesIO(oss_bucket.get_object(path).read())
+ src_state = torch.load(buffer, map_location=map_location)
+ if "state_dict" in src_state:
+ src_state = src_state["state_dict"]
+
+ for k in dst_state.keys():
+ if not k.startswith("module.") and "module." + k in src_state.keys():
+ k_ddp = "module." + k
+ else:
+ k_ddp = k
+ if k_ddp in src_state:
+ dst_state[k] = src_state[k_ddp]
+ else:
+ print(f"Miss key in ckpt: model: {k}, ckpt: {k_ddp}")
+
+ flag = obj.load_state_dict(dst_state, strict=True)
+ # print(flag)
+
+
+# def load_pretrained_model(
+# path: str,
+# model: torch.nn.Module,
+# ignore_init_mismatch: bool,
+# map_location: str = "cpu",
+# oss_bucket=None,
+# scope_map=None,
+# excludes=None,
+# ):
+# """Load a model state and set it to the model.
+#
+# Args:
+# init_param: :::
+#
+# Examples:
+#
+# """
+#
+# obj = model
+#
+# if oss_bucket is None:
+# src_state = torch.load(path, map_location=map_location)
+# else:
+# buffer = BytesIO(oss_bucket.get_object(path).read())
+# src_state = torch.load(buffer, map_location=map_location)
+# src_state = src_state["model"] if "model" in src_state else src_state
+#
+# if excludes is not None:
+# for e in excludes.split(","):
+# src_state = {k: v for k, v in src_state.items() if not k.startswith(e)}
+#
+# dst_state = obj.state_dict()
+# src_state = assigment_scope_map(dst_state, src_state, scope_map)
+#
+# if ignore_init_mismatch:
+# src_state = filter_state_dict(dst_state, src_state)
+#
+# logging.debug("Loaded src_state keys: {}".format(src_state.keys()))
+# logging.debug("Loaded dst_state keys: {}".format(dst_state.keys()))
+# dst_state.update(src_state)
+# obj.load_state_dict(dst_state, strict=True)
diff --git a/almeval/models/stepaudio/funasr_detach/train_utils/model_summary.py b/almeval/models/stepaudio/funasr_detach/train_utils/model_summary.py
new file mode 100644
index 0000000000000000000000000000000000000000..1001160a9dafd4c3235c7477d88616976686ff3b
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/train_utils/model_summary.py
@@ -0,0 +1,64 @@
+import numpy as np
+import torch
+
+
+def get_human_readable_count(number: int) -> str:
+ """Return human_readable_count
+
+ Originated from:
+ https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pytorch_lightning/core/memory.py
+
+ Abbreviates an integer number with K, M, B, T for thousands, millions,
+ billions and trillions, respectively.
+ Examples:
+ >>> get_human_readable_count(123)
+ '123 '
+ >>> get_human_readable_count(1234) # (one thousand)
+ '1 K'
+ >>> get_human_readable_count(2e6) # (two million)
+ '2 M'
+ >>> get_human_readable_count(3e9) # (three billion)
+ '3 B'
+ >>> get_human_readable_count(4e12) # (four trillion)
+ '4 T'
+ >>> get_human_readable_count(5e15) # (more than trillion)
+ '5,000 T'
+ Args:
+ number: a positive integer number
+ Return:
+ A string formatted according to the pattern described above.
+ """
+ assert number >= 0
+ labels = [" ", "K", "M", "B", "T"]
+ num_digits = int(np.floor(np.log10(number)) + 1 if number > 0 else 1)
+ num_groups = int(np.ceil(num_digits / 3))
+ num_groups = min(num_groups, len(labels)) # don't abbreviate beyond trillions
+ shift = -3 * (num_groups - 1)
+ number = number * (10**shift)
+ index = num_groups - 1
+ return f"{number:.2f} {labels[index]}"
+
+
+def to_bytes(dtype) -> int:
+ # torch.float16 -> 16
+ return int(str(dtype)[-2:]) // 8
+
+
+def model_summary(model: torch.nn.Module) -> str:
+ message = "Model structure:\n"
+ message += str(model)
+ tot_params = sum(p.numel() for p in model.parameters())
+ num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+ percent_trainable = "{:.1f}".format(num_params * 100.0 / tot_params)
+ tot_params = get_human_readable_count(tot_params)
+ num_params = get_human_readable_count(num_params)
+ message += "\n\nModel summary:\n"
+ message += f" Class Name: {model.__class__.__name__}\n"
+ message += f" Total Number of model parameters: {tot_params}\n"
+ message += (
+ f" Number of trainable parameters: {num_params} ({percent_trainable}%)\n"
+ )
+
+ dtype = next(iter(model.parameters())).dtype
+ message += f" Type: {dtype}"
+ return message
diff --git a/almeval/models/stepaudio/funasr_detach/train_utils/recursive_op.py b/almeval/models/stepaudio/funasr_detach/train_utils/recursive_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c2a92fd83b6efd96b9b42d906418c8301c3259c
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/train_utils/recursive_op.py
@@ -0,0 +1,48 @@
+"""Torch utility module."""
+
+import torch
+
+if torch.distributed.is_available():
+ from torch.distributed import ReduceOp
+
+
+def recursive_sum(obj, weight: torch.Tensor, distributed: bool = False):
+ assert weight.dim() == 1, weight.size()
+ if isinstance(obj, (tuple, list)):
+ return type(obj)(recursive_sum(v, weight, distributed) for v in obj)
+ elif isinstance(obj, dict):
+ return {k: recursive_sum(v, weight, distributed) for k, v in obj.items()}
+ elif isinstance(obj, torch.Tensor):
+ assert obj.size() == weight.size(), (obj.size(), weight.size())
+ obj = (obj * weight.type(obj.dtype)).sum()
+ if distributed:
+ torch.distributed.all_reduce(obj, op=ReduceOp.SUM)
+ return obj
+ elif obj is None:
+ return None
+ else:
+ raise ValueError(type(obj))
+
+
+def recursive_divide(a, b: torch.Tensor):
+ if isinstance(a, (tuple, list)):
+ return type(a)(recursive_divide(v, b) for v in a)
+ elif isinstance(a, dict):
+ return {k: recursive_divide(v, b) for k, v in a.items()}
+ elif isinstance(a, torch.Tensor):
+ assert a.size() == b.size(), (a.size(), b.size())
+ return a / b.type(a.dtype)
+ elif a is None:
+ return None
+ else:
+ raise ValueError(type(a))
+
+
+def recursive_average(obj, weight: torch.Tensor, distributed: bool = False):
+ obj = recursive_sum(obj, weight, distributed)
+ weight = weight.sum()
+ if distributed:
+ torch.distributed.all_reduce(weight, op=ReduceOp.SUM)
+ # Normalize weight to be sum-to-1
+ obj = recursive_divide(obj, weight)
+ return obj, weight
diff --git a/almeval/models/stepaudio/funasr_detach/train_utils/set_all_random_seed.py b/almeval/models/stepaudio/funasr_detach/train_utils/set_all_random_seed.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebdca3f537aac53bdc6e6cea168c49805bdf2d2f
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/train_utils/set_all_random_seed.py
@@ -0,0 +1,10 @@
+import random
+
+import numpy as np
+import torch
+
+
+def set_all_random_seed(seed: int):
+ random.seed(seed)
+ np.random.seed(seed)
+ torch.random.manual_seed(seed)
diff --git a/almeval/models/stepaudio/funasr_detach/train_utils/trainer.py b/almeval/models/stepaudio/funasr_detach/train_utils/trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6f218efe9f735fdb1f2903f031d856f62a3e573
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/train_utils/trainer.py
@@ -0,0 +1,417 @@
+import os
+import time
+import torch
+import logging
+from tqdm import tqdm
+from datetime import datetime
+import torch.distributed as dist
+from contextlib import nullcontext
+
+# from torch.utils.tensorboard import SummaryWriter
+from tensorboardX import SummaryWriter
+from pathlib import Path
+
+from funasr_detach.train_utils.device_funcs import to_device
+from funasr_detach.train_utils.recursive_op import recursive_average
+from funasr_detach.train_utils.average_nbest_models import average_checkpoints
+
+
+class Trainer:
+ """
+ A simple trainer class for training a PyTorch model, saving checkpoints at the end of each epoch,
+ and optionally resuming from a saved checkpoint.
+
+ Attributes:
+ max_epoch (int): Maximum number of epochs for training.
+ model (torch.nn.Module): The model to be trained.
+ optim (torch.optim.Optimizer): The optimizer to use for training.
+ scheduler (torch.optim.lr_scheduler._LRScheduler): The learning rate scheduler.
+ dataloader_train (torch.utils.data.DataLoader): DataLoader for the training dataset.
+ dataloader_val (torch.utils.data.DataLoader): DataLoader for the validation dataset.
+ output_dir (str): Directory where model checkpoints will be saved.
+ resume (str, optional): Path to a checkpoint to resume training from.
+ """
+
+ def __init__(
+ self,
+ model,
+ optim,
+ scheduler,
+ dataloader_train,
+ dataloader_val,
+ local_rank,
+ use_ddp=False,
+ use_fsdp=False,
+ output_dir: str = "./",
+ **kwargs,
+ ):
+ """
+ Initializes the Trainer class with the model, optimizer, scheduler, dataloaders, and other settings.
+
+ Args:
+ model (torch.nn.Module): The model to be trained.
+ optim (torch.optim.Optimizer): The optimizer to use for training.
+ scheduler (torch.optim.lr_scheduler._LRScheduler): The learning rate scheduler.
+ dataloader_train (torch.utils.data.DataLoader): The DataLoader for the training dataset.
+ dataloader_val (torch.utils.data.DataLoader): The DataLoader for the validation dataset.
+ **kwargs: Additional keyword arguments:
+ max_epoch (int): The maximum number of epochs for training.
+ output_dir (str): The directory where model checkpoints will be saved. Default is './'.
+ resume (str, optional): The file path to a checkpoint to resume training from.
+ """
+
+ self.model = model
+ self.optim = optim
+ self.scheduler = scheduler
+ self.dataloader_train = dataloader_train
+ self.dataloader_val = dataloader_val
+ self.output_dir = output_dir
+ self.resume = kwargs.get("resume", True)
+ self.start_epoch = 0
+ self.max_epoch = kwargs.get("max_epoch", 100)
+ self.local_rank = local_rank
+ self.use_ddp = use_ddp
+ self.use_fsdp = use_fsdp
+ self.device = next(model.parameters()).device
+ self.avg_nbest_model = kwargs.get("avg_nbest_model", 5)
+ self.kwargs = kwargs
+ self.log_interval = kwargs.get("log_interval", 50)
+ self.batch_total = 0
+
+ try:
+ rank = dist.get_rank()
+ world_size = dist.get_world_size()
+ except:
+ rank = 0
+ world_size = 1
+ logging.warning("distributed is not initialized, only single shard")
+ self.rank = rank
+ self.world_size = world_size
+
+ os.makedirs(os.path.join(self.output_dir, "tensorboard"), exist_ok=True)
+ self.writer = (
+ SummaryWriter(os.path.join(self.output_dir, "tensorboard"))
+ if rank == 0
+ else None
+ )
+
+ def _save_checkpoint(self, epoch):
+ """
+ Saves a checkpoint containing the model's state, the optimizer's state,
+ and the scheduler's state at the end of the given epoch. This method is
+ intended to be called at the end of each epoch to save the training progress.
+
+ Args:
+ epoch (int): The epoch number at which the checkpoint is being saved.
+ """
+ state = {
+ "epoch": epoch,
+ "state_dict": self.model.state_dict(),
+ "optimizer": self.optim.state_dict(),
+ "scheduler": self.scheduler.state_dict(),
+ }
+ # Create output directory if it does not exist
+ os.makedirs(self.output_dir, exist_ok=True)
+ filename = os.path.join(self.output_dir, f"model.pt.ep{epoch}")
+ torch.save(state, filename)
+
+ print(f"\nCheckpoint saved to {filename}\n")
+ latest = Path(os.path.join(self.output_dir, f"model.pt"))
+ torch.save(state, latest)
+
+ def _resume_checkpoint(self, resume_path):
+ """
+ Resumes training from a checkpoint at the given file path.
+ Loads the model's state, the optimizer's state, and the scheduler's state.
+
+ Args:
+ resume_path (str): The file path to the checkpoint to resume from.
+ """
+ ckpt = os.path.join(resume_path, "model.pt")
+ if os.path.isfile(ckpt):
+ checkpoint = torch.load(ckpt)
+ self.start_epoch = checkpoint["epoch"] + 1
+ # self.model.load_state_dict(checkpoint['state_dict'])
+ src_state = checkpoint["state_dict"]
+ dst_state = self.model.state_dict()
+ for k in dst_state.keys():
+ if not k.startswith("module.") and "module." + k in src_state.keys():
+ k_ddp = "module." + k
+ else:
+ k_ddp = k
+ if k_ddp in src_state.keys():
+ dst_state[k] = src_state[k_ddp]
+ else:
+ print(f"Miss key in ckpt: model: {k}, ckpt: {k_ddp}")
+
+ self.model.load_state_dict(dst_state)
+ self.optim.load_state_dict(checkpoint["optimizer"])
+ self.scheduler.load_state_dict(checkpoint["scheduler"])
+ print(f"Checkpoint loaded successfully from '{ckpt}'")
+ else:
+ print(f"No checkpoint found at '{ckpt}', starting from scratch")
+
+ if self.use_ddp or self.use_fsdp:
+ dist.barrier()
+
+ def run(self):
+ """
+ Starts the training process, iterating over epochs, training the model,
+ and saving checkpoints at the end of each epoch.
+ """
+ if self.resume:
+ self._resume_checkpoint(self.output_dir)
+
+ for epoch in range(self.start_epoch, self.max_epoch + 1):
+ time1 = time.perf_counter()
+ self._train_epoch(epoch)
+
+ if self.use_ddp or self.use_fsdp:
+ dist.barrier()
+
+ self._validate_epoch(epoch)
+
+ if self.use_ddp or self.use_fsdp:
+ dist.barrier()
+
+ if self.rank == 0:
+ self._save_checkpoint(epoch)
+
+ if self.use_ddp or self.use_fsdp:
+ dist.barrier()
+
+ self.scheduler.step()
+
+ time2 = time.perf_counter()
+ time_escaped = (time2 - time1) / 3600.0
+ print(
+ f"\nrank: {self.local_rank}, time_escaped_epoch: {time_escaped:.3f} hours, estimated to finish {self.max_epoch} epoch: {(self.max_epoch-epoch)*time_escaped:.3f}\n"
+ )
+
+ if self.rank == 0:
+ average_checkpoints(self.output_dir, self.avg_nbest_model)
+
+ if self.use_ddp or self.use_fsdp:
+ dist.barrier()
+
+ if self.writer:
+ self.writer.close()
+
+ def _train_epoch(self, epoch):
+ """
+ Defines the training process for a single epoch with gradient accumulation.
+ Args:
+ epoch (int): The current epoch number.
+ """
+ self.model.train()
+ pbar = tqdm(
+ colour="blue",
+ desc=f"rank: {self.local_rank}, Training Epoch: {epoch + 1}",
+ total=len(self.dataloader_train),
+ dynamic_ncols=True,
+ )
+
+ # Set the number of steps for gradient accumulation
+ accum_grad = self.kwargs.get("accum_grad", 1)
+ # Initialize the gradient accumulation
+ self.optim.zero_grad()
+ speed_stats = {}
+ time5 = time.perf_counter()
+
+ for batch_idx, batch in enumerate(self.dataloader_train):
+ self.batch_total += 1
+ time1 = time.perf_counter()
+ speed_stats["data_load"] = f"{time1-time5:0.3f}"
+
+ batch = to_device(batch, self.device)
+
+ my_context = (
+ self.model.no_sync if batch_idx % accum_grad != 0 else nullcontext
+ )
+ with my_context():
+ time2 = time.perf_counter()
+
+ retval = self.model(**batch)
+ torch.cuda.empty_cache()
+
+ time3 = time.perf_counter()
+ speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
+ loss, stats, weight = retval
+ stats = {k: v for k, v in stats.items() if v is not None}
+ if self.use_ddp or self.use_fsdp:
+ # Apply weighted averaging for loss and stats
+ loss = (loss * weight.type(loss.dtype)).sum()
+ # if distributed, this method can also apply all_reduce()
+ stats, weight = recursive_average(stats, weight, distributed=True)
+ # Now weight is summation over all workers
+ loss /= weight
+ # Multiply world_size because DistributedDataParallel
+ # automatically normalizes the gradient by world_size.
+ loss *= self.world_size
+ # Scale the loss since we're not updating for every mini-batch
+ loss = loss / accum_grad
+ loss.backward()
+ time4 = time.perf_counter()
+ speed_stats["backward_time"] = f"{time4 - time3:0.3f}"
+
+ # Perform an optimizer step only after accumulating enough gradients
+ if (batch_idx + 1) % accum_grad == 0 or (batch_idx + 1) == len(
+ self.dataloader_train
+ ):
+ # Perform gradient clipping if it is set
+ if self.kwargs.get("grad_clip", None) is not None:
+ grad_norm = torch.nn.utils.clip_grad_norm_(
+ self.model.parameters(),
+ max_norm=self.kwargs.get("grad_clip", 10.0),
+ norm_type=self.kwargs.get("grad_clip_type", 2.0),
+ )
+ if not torch.isfinite(grad_norm):
+ logging.warning(
+ f"The grad norm is {grad_norm}. Skipping updating the model."
+ )
+ self.optim.zero_grad() # Reset gradients
+ continue
+
+ # Execute an optimization step (update model parameters)
+ if self.use_ddp or self.use_fsdp:
+ dist.barrier()
+ self.optim.step()
+ self.scheduler.step()
+ # Clear gradients for the next accumulation stage
+ self.optim.zero_grad()
+ total_time = f"{time.perf_counter() - time5:0.3f}"
+ time5 = time.perf_counter()
+ speed_stats["optim_time"] = f"{time5 - time4:0.3f}"
+
+ speed_stats["total_time"] = total_time
+
+ if (batch_idx + 1) % self.log_interval == 0 or (batch_idx + 1) == len(
+ self.dataloader_train
+ ):
+ pbar.update(self.log_interval)
+ gpu_info = (
+ "GPU, memory: {:.3f} GB, "
+ "{:.3f} GB, "
+ "{:.3f} GB, "
+ "{:.3f} GB".format(
+ torch.cuda.memory_allocated() / 1024 / 1024 / 1024,
+ torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024,
+ torch.cuda.memory_reserved() / 1024 / 1024 / 1024,
+ torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024,
+ )
+ )
+ lr = self.scheduler.get_last_lr()[0]
+ time_now = datetime.now()
+ time_now = time_now.strftime("%Y-%m-%d %H:%M:%S")
+ description = (
+ f"{time_now}, "
+ f"rank: {self.local_rank}, "
+ f"epoch: {epoch}/{self.max_epoch}, "
+ f"step: {batch_idx+1}/{len(self.dataloader_train)}, total: {self.batch_total}, "
+ f"(loss: {loss.detach().cpu().item():.3f}), "
+ f"(lr: {lr:.3e}), "
+ f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, "
+ f"{speed_stats}, "
+ f"{gpu_info}"
+ )
+ pbar.set_description(description)
+ if self.writer:
+ self.writer.add_scalar(
+ f"rank{self.local_rank}_Loss/train",
+ loss.item(),
+ self.batch_total,
+ )
+ self.writer.add_scalar(
+ f"rank{self.local_rank}_lr/train", lr, self.batch_total
+ )
+ for key, var in stats.items():
+ self.writer.add_scalar(
+ f"rank{self.local_rank}_{key}/train",
+ var.item(),
+ self.batch_total,
+ )
+ for key, var in speed_stats.items():
+ self.writer.add_scalar(
+ f"rank{self.local_rank}_{key}/train",
+ eval(var),
+ self.batch_total,
+ )
+
+ pbar.close()
+
+ def _validate_epoch(self, epoch):
+ """
+ Defines the validation process for a single epoch.
+ Should be implemented with the actual model validation steps.
+
+ Args:
+ epoch (int): The current epoch number.
+ """
+ self.model.eval()
+ with torch.no_grad():
+ pbar = tqdm(
+ colour="red",
+ desc=f"rank: {self.local_rank}, Validation Epoch: {epoch + 1}",
+ total=len(self.dataloader_val),
+ dynamic_ncols=True,
+ )
+ speed_stats = {}
+ time5 = time.perf_counter()
+ for batch_idx, batch in enumerate(self.dataloader_val):
+ time1 = time.perf_counter()
+ speed_stats["data_load"] = f"{time1 - time5:0.3f}"
+ batch = to_device(batch, self.device)
+ time2 = time.perf_counter()
+ retval = self.model(**batch)
+ time3 = time.perf_counter()
+ speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
+ loss, stats, weight = retval
+ stats = {k: v for k, v in stats.items() if v is not None}
+ if self.use_ddp or self.use_fsdp:
+ # Apply weighted averaging for loss and stats
+ loss = (loss * weight.type(loss.dtype)).sum()
+ # if distributed, this method can also apply all_reduce()
+ stats, weight = recursive_average(stats, weight, distributed=True)
+ # Now weight is summation over all workers
+ loss /= weight
+ # Multiply world_size because DistributedDataParallel
+ # automatically normalizes the gradient by world_size.
+ loss *= self.world_size
+ # Scale the loss since we're not updating for every mini-batch
+ loss = loss
+ time4 = time.perf_counter()
+
+ if (batch_idx + 1) % self.log_interval == 0 or (batch_idx + 1) == len(
+ self.dataloader_val
+ ):
+ pbar.update(self.log_interval)
+ time_now = datetime.now()
+ time_now = time_now.strftime("%Y-%m-%d %H:%M:%S")
+ description = (
+ f"{time_now}, "
+ f"rank: {self.local_rank}, "
+ f"validation epoch: {epoch}/{self.max_epoch}, "
+ f"step: {batch_idx+1}/{len(self.dataloader_val)}, "
+ f"(loss: {loss.detach().cpu().item():.3f}), "
+ f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, "
+ f"{speed_stats}, "
+ )
+ pbar.set_description(description)
+ if self.writer:
+ self.writer.add_scalar(
+ f"rank{self.local_rank}_Loss/val",
+ loss.item(),
+ epoch * len(self.dataloader_val) + batch_idx,
+ )
+ for key, var in stats.items():
+ self.writer.add_scalar(
+ f"rank{self.local_rank}_{key}/val",
+ var.item(),
+ epoch * len(self.dataloader_val) + batch_idx,
+ )
+ for key, var in speed_stats.items():
+ self.writer.add_scalar(
+ f"rank{self.local_rank}_{key}/val",
+ eval(var),
+ epoch * len(self.dataloader_val) + batch_idx,
+ )
diff --git a/almeval/models/stepaudio/funasr_detach/version.txt b/almeval/models/stepaudio/funasr_detach/version.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b0f3d96f877256ed9ae03858ecc5185a989b1d1b
--- /dev/null
+++ b/almeval/models/stepaudio/funasr_detach/version.txt
@@ -0,0 +1 @@
+1.0.8
diff --git a/almeval/models/stepaudio/requirements-vllm.txt b/almeval/models/stepaudio/requirements-vllm.txt
new file mode 100644
index 0000000000000000000000000000000000000000..78b53c2bb6b3ebb4a5f535c47016ef8f774e1434
--- /dev/null
+++ b/almeval/models/stepaudio/requirements-vllm.txt
@@ -0,0 +1,22 @@
+torch==2.5.1
+torchaudio==2.5.1
+torchvision
+transformers==4.48.3
+accelerate==1.3.0
+openai-whisper
+onnxruntime-gpu==1.19.0
+omegaconf==2.3.0
+librosa==0.10.2.post1
+sox==1.5.0
+modelscope
+numpy==1.26.4
+six==1.16.0
+hyperpyyaml
+conformer==0.3.2
+diffusers
+pillow
+sentencepiece
+funasr>=1.1.3
+protobuf==5.29.3
+gradio>=5.16.0
+vllm==0.7.2
\ No newline at end of file
diff --git a/almeval/models/stepaudio/requirements.txt b/almeval/models/stepaudio/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2aa7689542e06d4db5913a6cfbf573b73c803b4c
--- /dev/null
+++ b/almeval/models/stepaudio/requirements.txt
@@ -0,0 +1,21 @@
+torch==2.3.1
+torchaudio==2.3.1
+torchvision==0.18.1
+transformers==4.48.3
+accelerate==1.3.0
+openai-whisper==20231117
+onnxruntime-gpu==1.17.0
+omegaconf==2.3.0
+librosa==0.10.2.post1
+sox==1.5.0
+modelscope
+numpy==1.26.4
+six==1.16.0
+hyperpyyaml
+conformer==0.3.2
+diffusers
+pillow
+sentencepiece
+funasr>=1.1.3
+protobuf==5.29.3
+gradio>=5.16.0
diff --git a/almeval/models/stepaudio/speakers/TingtingRAP_prompt.wav b/almeval/models/stepaudio/speakers/TingtingRAP_prompt.wav
new file mode 100644
index 0000000000000000000000000000000000000000..55474b9252779ee572650dd4a68be6011d18f3e7
--- /dev/null
+++ b/almeval/models/stepaudio/speakers/TingtingRAP_prompt.wav
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36d260f1a62d1d96b8d5a01b14a197ad967e7652738d6d4cee25816121c4c730
+size 1042174
diff --git a/almeval/models/stepaudio/speakers/Tingting_prompt.wav b/almeval/models/stepaudio/speakers/Tingting_prompt.wav
new file mode 100644
index 0000000000000000000000000000000000000000..e3f1183166690080fb00ff9cbcf3d0b44deb84de
--- /dev/null
+++ b/almeval/models/stepaudio/speakers/Tingting_prompt.wav
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:27ecbef5f3fb6609174ec9556623825867ec5fad485c9641cca41893c4966fc2
+size 394196
diff --git "a/almeval/models/stepaudio/speakers/Tingting\345\223\274\345\224\261_prompt.wav" "b/almeval/models/stepaudio/speakers/Tingting\345\223\274\345\224\261_prompt.wav"
new file mode 100644
index 0000000000000000000000000000000000000000..3d6ded0476f42c31a552f52a546fa4478d00d5b4
--- /dev/null
+++ "b/almeval/models/stepaudio/speakers/Tingting\345\223\274\345\224\261_prompt.wav"
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b8455e31e51ce0cafd78f36f3b726f8dfdfc6171419c68ebde1ab72c3621581
+size 1801518
diff --git a/almeval/models/stepaudio/tokenizer.py b/almeval/models/stepaudio/tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad606bcb0c00930e6f0464787f5140d88f9a422f
--- /dev/null
+++ b/almeval/models/stepaudio/tokenizer.py
@@ -0,0 +1,197 @@
+import io
+import threading
+import time
+import os
+
+import numpy as np
+import torch
+import torchaudio
+import onnxruntime
+import whisper
+
+from funasr_detach import AutoModel
+from utils import resample_audio, energy_norm_fn, trim_silence
+
+
+class StepAudioTokenizer:
+ def __init__(
+ self,
+ encoder_path,
+ ):
+ funasr_model_path = os.path.join(
+ encoder_path,
+ "dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online",
+ )
+ kms_path = os.path.join(encoder_path, "linguistic_tokenizer.npy")
+ cosy_tokenizer_path = os.path.join(encoder_path, "speech_tokenizer_v1.onnx")
+ self.funasr_model = AutoModel(model=funasr_model_path, model_revision="master")
+ self.kms = torch.tensor(np.load(kms_path))
+
+ providers = ["CUDAExecutionProvider"]
+ session_option = onnxruntime.SessionOptions()
+ session_option.graph_optimization_level = (
+ onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+ )
+ session_option.intra_op_num_threads = 1
+ self.ort_session = onnxruntime.InferenceSession(
+ cosy_tokenizer_path, sess_options=session_option, providers=providers
+ )
+ self.chunk_size = [0, 4, 5]
+ self.encoder_chunk_look_back = 4
+ self.decoder_chunk_look_back = 1
+
+ self.vq02_sessions = {}
+ self.vq02_lock = threading.Lock()
+ self.vq06_lock = threading.Lock()
+
+ def __call__(self, audio, sr):
+ _, vq02, vq06 = self.wav2token(audio, sr, False)
+ text = self.merge_vq0206_to_token_str(vq02, vq06)
+ return text
+
+ def preprocess_wav(self, audio, sample_rate, enable_trim=True, energy_norm=True):
+ audio = resample_audio(audio, sample_rate, 16000)
+ if energy_norm:
+ audio = energy_norm_fn(audio)
+
+ if enable_trim:
+ audio = audio.cpu().numpy().squeeze(0)
+ audio = trim_silence(audio, 16000)
+ audio = torch.from_numpy(audio)
+ audio = audio.unsqueeze(0)
+ return audio
+
+ def wav2token(self, audio, sample_rate, enable_trim=True, energy_norm=True):
+ audio = self.preprocess_wav(
+ audio, sample_rate, enable_trim=enable_trim, energy_norm=energy_norm
+ )
+
+ vq02_ori = self.get_vq02_code(audio)
+ vq02 = [int(x) + 65536 for x in vq02_ori]
+ vq06_ori = self.get_vq06_code(audio)
+ vq06 = [int(x) + 65536 + 1024 for x in vq06_ori]
+
+ chunk = 1
+ chunk_nums = min(len(vq06) // (3 * chunk), len(vq02) // (2 * chunk))
+ speech_tokens = []
+ for idx in range(chunk_nums):
+ speech_tokens += vq02[idx * chunk * 2 : (idx + 1) * chunk * 2]
+ speech_tokens += vq06[idx * chunk * 3 : (idx + 1) * chunk * 3]
+ return speech_tokens, vq02_ori, vq06_ori
+
+ def get_vq02_code(self, audio, session_id=None, is_final=True):
+ _tmp_wav = io.BytesIO()
+ torchaudio.save(_tmp_wav, audio, 16000, format="wav")
+ _tmp_wav.seek(0)
+
+ with self.vq02_lock:
+ cache = {}
+ if session_id in self.vq02_sessions:
+ cache = self.vq02_sessions[session_id].get("cache", {})
+
+ res, new_cache = self.funasr_model.infer_encoder(
+ input=[_tmp_wav],
+ chunk_size=self.chunk_size,
+ encoder_chunk_look_back=self.encoder_chunk_look_back,
+ decoder_chunk_look_back=self.decoder_chunk_look_back,
+ device=0,
+ is_final=is_final,
+ cache=cache,
+ )
+ c_list = []
+ for j, res_ in enumerate(res):
+ feat = res_["enc_out"]
+ if len(feat) > 0:
+ c_list = self.dump_label([feat], self.kms)[0]
+
+ if is_final:
+ if session_id in self.vq02_sessions:
+ self.vq02_sessions.pop(session_id)
+ else:
+ if isinstance(session_id, str) and len(session_id) > 0:
+ self.vq02_sessions[session_id] = {
+ "cache": new_cache,
+ "update_time": time.time(),
+ }
+
+ return c_list
+
+ def get_vq06_code(self, audio):
+
+ def split_audio(audio, chunk_duration=480000):
+ start = 0
+ chunks = []
+ while start < len(audio):
+ end = min(start + chunk_duration, len(audio))
+ chunk = audio[start:end]
+ if len(chunk) < 480:
+ pass
+ else:
+ chunks.append(chunk)
+ start = end
+ return chunks
+
+ with self.vq06_lock:
+ audio = audio.squeeze(0)
+ chunk_audios = split_audio(audio, chunk_duration=30 * 16000) # 最大支持30s
+ speech_tokens = []
+ for chunk in chunk_audios:
+ duration = round(chunk.shape[0] / 16000, 2)
+ feat = whisper.log_mel_spectrogram(chunk, n_mels=128)
+ feat = feat.unsqueeze(0)
+ feat_len = np.array([feat.shape[2]], dtype=np.int32)
+ chunk_token = (
+ self.ort_session.run(
+ None,
+ {
+ self.ort_session.get_inputs()[0]
+ .name: feat.detach()
+ .cpu()
+ .numpy(),
+ self.ort_session.get_inputs()[1].name: feat_len,
+ },
+ )[0]
+ .flatten()
+ .tolist()
+ )
+ assert abs(len(chunk_token) - duration * 25) <= 2
+ speech_tokens += chunk_token
+
+ return speech_tokens
+
+ def kmean_cluster(self, samples, means):
+ dists = torch.cdist(samples, means)
+ indices = dists.argmin(dim=1).cpu().numpy()
+ return indices.tolist()
+
+ def dump_label(self, samples, mean):
+ dims = samples[0].shape[-1]
+ x_lens = [x.shape[1] for x in samples]
+ total_len = sum(x_lens)
+ x_sel = torch.FloatTensor(1, total_len, dims)
+ start_len = 0
+ for sample in samples:
+ sample_len = sample.shape[1]
+ end_len = start_len + sample_len
+ x_sel[:, start_len:end_len] = sample
+ start_len = end_len
+ dense_x = x_sel.squeeze(0)
+ indices = self.kmean_cluster(dense_x, mean)
+ indices_list = []
+ start_len = 0
+ for x_len in x_lens:
+ end_len = start_len + end_len
+ indices_list.append(indices[start_len:end_len])
+ return indices_list
+
+ def merge_vq0206_to_token_str(self, vq02, vq06):
+ _vq06 = [1024 + x for x in vq06]
+ result = []
+ i = 0
+ j = 0
+ while i < len(vq02) - 1 and j < len(_vq06) - 2:
+ sublist = vq02[i : i + 2] + _vq06[j : j + 3]
+ result.extend(sublist)
+ i += 2
+ j += 3
+ return "".join([f"" for x in result])
diff --git a/almeval/models/stepaudio/tts.py b/almeval/models/stepaudio/tts.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e6a86e1747af28b83b8134688e192293ff0e2a0
--- /dev/null
+++ b/almeval/models/stepaudio/tts.py
@@ -0,0 +1,230 @@
+import os
+import re
+import json
+
+import torchaudio
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation.logits_process import LogitsProcessor
+from transformers.generation.utils import LogitsProcessorList
+
+from cosyvoice.cli.cosyvoice import CosyVoice
+from utils import load_optimus_ths_lib
+
+class RepetitionAwareLogitsProcessor(LogitsProcessor):
+ def __call__(
+ self, input_ids: torch.LongTensor, scores: torch.FloatTensor
+ ) -> torch.FloatTensor:
+ window_size = 10
+ threshold = 0.1
+
+ window = input_ids[:, -window_size:]
+ if window.shape[1] < window_size:
+ return scores
+
+ last_tokens = window[:, -1].unsqueeze(-1)
+ repeat_counts = (window == last_tokens).sum(dim=1)
+ repeat_ratios = repeat_counts.float() / window_size
+
+ mask = repeat_ratios > threshold
+ scores[mask, last_tokens[mask].squeeze(-1)] = float("-inf")
+ return scores
+
+
+class StepAudioTTS:
+ def __init__(
+ self,
+ model_path,
+ encoder,
+ ):
+ load_optimus_ths_lib(os.path.join(model_path, 'lib'))
+ self.llm = AutoModelForCausalLM.from_pretrained(
+ model_path,
+ torch_dtype=torch.bfloat16,
+ device_map="cuda",
+ trust_remote_code=True,
+ )
+ self.tokenizer = AutoTokenizer.from_pretrained(
+ model_path, trust_remote_code=True
+ )
+ self.common_cosy_model = CosyVoice(
+ os.path.join(model_path, "CosyVoice-300M-25Hz")
+ )
+ self.music_cosy_model = CosyVoice(
+ os.path.join(model_path, "CosyVoice-300M-25Hz-Music")
+ )
+ self.encoder = encoder
+ self.sys_prompt_dict = {
+ "sys_prompt_for_rap": "请参考对话历史里的音色,用RAP方式将文本内容大声说唱出来。",
+ "sys_prompt_for_vocal": "请参考对话历史里的音色,用哼唱的方式将文本内容大声唱出来。",
+ "sys_prompt_wo_spk": '作为一名卓越的声优演员,你的任务是根据文本中()或()括号内标注的情感、语种或方言、音乐哼唱、语音调整等标签,以丰富细腻的情感和自然顺畅的语调来朗读文本。\n# 情感标签涵盖了多种情绪状态,包括但不限于:\n- "高兴1"\n- "高兴2"\n- "生气1"\n- "生气2"\n- "悲伤1"\n- "撒娇1"\n\n# 语种或方言标签包含多种语言或方言,包括但不限于:\n- "中文"\n- "英文"\n- "韩语"\n- "日语"\n- "四川话"\n- "粤语"\n- "广东话"\n\n# 音乐哼唱标签包含多种类型歌曲哼唱,包括但不限于:\n- "RAP"\n- "哼唱"\n\n# 语音调整标签,包括但不限于:\n- "慢速1"\n- "慢速2"\n- "快速1"\n- "快速2"\n\n请在朗读时,根据这些情感标签的指示,调整你的情感、语气、语调和哼唱节奏,以确保文本的情感和意义得到准确而生动的传达,如果没有()或()括号,则根据文本语义内容自由演绎。',
+ "sys_prompt_with_spk": '作为一名卓越的声优演员,你的任务是根据文本中()或()括号内标注的情感、语种或方言、音乐哼唱、语音调整等标签,以丰富细腻的情感和自然顺畅的语调来朗读文本。\n# 情感标签涵盖了多种情绪状态,包括但不限于:\n- "高兴1"\n- "高兴2"\n- "生气1"\n- "生气2"\n- "悲伤1"\n- "撒娇1"\n\n# 语种或方言标签包含多种语言或方言,包括但不限于:\n- "中文"\n- "英文"\n- "韩语"\n- "日语"\n- "四川话"\n- "粤语"\n- "广东话"\n\n# 音乐哼唱标签包含多种类型歌曲哼唱,包括但不限于:\n- "RAP"\n- "哼唱"\n\n# 语音调整标签,包括但不限于:\n- "慢速1"\n- "慢速2"\n- "快速1"\n- "快速2"\n\n请在朗读时,使用[{}]的声音,根据这些情感标签的指示,调整你的情感、语气、语调和哼唱节奏,以确保文本的情感和意义得到准确而生动的传达,如果没有()或()括号,则根据文本语义内容自由演绎。',
+ }
+ self.register_speakers()
+
+ def __call__(self, text: str, prompt_speaker: str, clone_dict: dict | None = None):
+ if clone_dict:
+ clone_prompt_code, clone_prompt_token, clone_prompt_token_len, clone_speech_feat, clone_speech_feat_len, clone_speech_embedding = (
+ self.preprocess_prompt_wav(clone_dict['wav_path'])
+ )
+ prompt_speaker = clone_dict['speaker']
+ clone_speakers_info = {
+ "prompt_text": clone_dict['prompt_text'],
+ "prompt_code": clone_prompt_code,
+ "cosy_speech_feat": clone_speech_feat.to(torch.bfloat16),
+ "cosy_speech_feat_len": clone_speech_feat_len,
+ "cosy_speech_embedding": clone_speech_embedding.to(torch.bfloat16),
+ "cosy_prompt_token": clone_prompt_token,
+ "cosy_prompt_token_len": clone_prompt_token_len,
+ }
+
+ instruction_name = self.detect_instruction_name(text)
+ prompt_speaker_info = self.speakers_info.get(prompt_speaker)
+ if instruction_name in ("RAP", "哼唱"):
+ if not clone_dict:
+ prompt_speaker_info = self.speakers_info[
+ f"{prompt_speaker}{instruction_name}"
+ ]
+ cosy_model = self.music_cosy_model
+ else:
+ cosy_model = self.common_cosy_model
+
+ if clone_dict:
+ prompt_speaker = ''
+ prompt_speaker_info = clone_speakers_info
+
+ token_ids = self.tokenize(
+ text,
+ prompt_speaker_info["prompt_text"],
+ prompt_speaker,
+ prompt_speaker_info["prompt_code"],
+ )
+ output_ids = self.llm.generate(
+ torch.tensor([token_ids]).to(torch.long).to("cuda"),
+ max_length=8192,
+ temperature=0.7,
+ do_sample=True,
+ logits_processor=LogitsProcessorList([RepetitionAwareLogitsProcessor()]),
+ )
+ output_ids = output_ids[:, len(token_ids) : -1] # skip eos token
+ return (
+ cosy_model.token_to_wav_offline(
+ output_ids - 65536,
+ prompt_speaker_info["cosy_speech_feat"].to(torch.bfloat16),
+ prompt_speaker_info["cosy_speech_feat_len"],
+ prompt_speaker_info["cosy_prompt_token"],
+ prompt_speaker_info["cosy_prompt_token_len"],
+ prompt_speaker_info["cosy_speech_embedding"].to(torch.bfloat16),
+ ),
+ 22050,
+ )
+
+ def register_speakers(self):
+ self.speakers_info = {}
+
+ with open("speakers/speakers_info.json", "r") as f:
+ speakers_info = json.load(f)
+
+ for speaker_id, prompt_text in speakers_info.items():
+ prompt_wav_path = f"speakers/{speaker_id}_prompt.wav"
+ prompt_code, prompt_token, prompt_token_len, speech_feat, speech_feat_len, speech_embedding = (
+ self.preprocess_prompt_wav(prompt_wav_path)
+ )
+
+ self.speakers_info[speaker_id] = {
+ "prompt_text": prompt_text,
+ "prompt_code": prompt_code,
+ "cosy_speech_feat": speech_feat.to(torch.bfloat16),
+ "cosy_speech_feat_len": speech_feat_len,
+ "cosy_speech_embedding": speech_embedding.to(torch.bfloat16),
+ "cosy_prompt_token": prompt_token,
+ "cosy_prompt_token_len": prompt_token_len,
+ }
+ print(f"Registered speaker: {speaker_id}")
+
+ def detect_instruction_name(self, text):
+ instruction_name = ""
+ match_group = re.match(r"^([(\(][^\(\)()]*[)\)]).*$", text, re.DOTALL)
+ if match_group is not None:
+ instruction = match_group.group(1)
+ instruction_name = instruction.strip("()()")
+ return instruction_name
+
+ def tokenize(
+ self, text: str, prompt_text: str, prompt_speaker: str, prompt_code: list
+ ):
+ rap_or_vocal = self.detect_instruction_name(text) in ("RAP", "哼唱")
+
+ if rap_or_vocal:
+ if "哼唱" in text:
+ prompt = self.sys_prompt_dict["sys_prompt_for_vocal"]
+ else:
+ prompt = self.sys_prompt_dict["sys_prompt_for_rap"]
+ elif prompt_speaker:
+ prompt = self.sys_prompt_dict["sys_prompt_with_spk"].format(prompt_speaker)
+ else:
+ prompt = self.sys_prompt_dict["sys_prompt_wo_spk"]
+
+ sys_tokens = self.tokenizer.encode(f"system\n{prompt}")
+
+ history = [1]
+ history.extend([4] + sys_tokens + [3])
+
+ _prefix_tokens = self.tokenizer.encode("\n")
+ prompt_token_encode = self.tokenizer.encode("\n" + prompt_text)
+ prompt_tokens = prompt_token_encode[len(_prefix_tokens) :]
+
+ target_token_encode = self.tokenizer.encode("\n" + text)
+ target_tokens = target_token_encode[len(_prefix_tokens) :]
+
+ qrole_toks = self.tokenizer.encode("human\n")
+ arole_toks = self.tokenizer.encode("assistant\n")
+
+ history.extend(
+ [4]
+ + qrole_toks
+ + prompt_tokens
+ + [3]
+ + [4]
+ + arole_toks
+ + prompt_code
+ + [3]
+ + [4]
+ + qrole_toks
+ + target_tokens
+ + [3]
+ + [4]
+ + arole_toks
+ )
+ return history
+
+ def preprocess_prompt_wav(self, prompt_wav_path : str):
+ prompt_wav, prompt_wav_sr = torchaudio.load(prompt_wav_path)
+ if prompt_wav.shape[0] > 1:
+ prompt_wav = prompt_wav.mean(dim=0, keepdim=True) # 将多通道音频转换为单通道
+ prompt_wav_16k = torchaudio.transforms.Resample(
+ orig_freq=prompt_wav_sr, new_freq=16000
+ )(prompt_wav)
+ prompt_wav_22k = torchaudio.transforms.Resample(
+ orig_freq=prompt_wav_sr, new_freq=22050
+ )(prompt_wav)
+
+ speech_feat, speech_feat_len = (
+ self.common_cosy_model.frontend._extract_speech_feat(prompt_wav_22k)
+ )
+ speech_embedding = self.common_cosy_model.frontend._extract_spk_embedding(
+ prompt_wav_16k
+ )
+
+ prompt_code, _, _ = self.encoder.wav2token(prompt_wav, prompt_wav_sr)
+ prompt_token = torch.tensor([prompt_code], dtype=torch.long) - 65536
+ prompt_token_len = torch.tensor([prompt_token.shape[1]], dtype=torch.long)
+
+ return (
+ prompt_code,
+ prompt_token,
+ prompt_token_len,
+ speech_feat,
+ speech_feat_len,
+ speech_embedding,
+ )
\ No newline at end of file
diff --git a/almeval/models/stepaudio/tts_app.py b/almeval/models/stepaudio/tts_app.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4963d4097e1db41b23e48003ec7fb0023a0fbf9
--- /dev/null
+++ b/almeval/models/stepaudio/tts_app.py
@@ -0,0 +1,208 @@
+import gradio as gr
+import argparse
+import torchaudio
+from tts import StepAudioTTS
+from tokenizer import StepAudioTokenizer
+from datetime import datetime
+import os
+
+
+# 保存音频
+def save_audio(audio_type, audio_data, sr):
+ current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+ save_path = os.path.join(args.tmp_dir, audio_type, f"{current_time}.wav")
+ os.makedirs(os.path.dirname(save_path), exist_ok=True)
+ torchaudio.save(save_path, audio_data, sr)
+ return save_path
+
+
+# 普通语音合成
+def tts_common(text, speaker, emotion, language, speed):
+ text = (
+ (f"({emotion})" if emotion else "")
+ + (f"({language})" if language else "")
+ + (f"({speed})" if speed else "")
+ + text
+ )
+ output_audio, sr = tts_engine(text, speaker)
+ audio_type = "common"
+ common_path = save_audio(audio_type, output_audio, sr)
+ return common_path
+
+
+# RAP / 哼唱模式
+def tts_music(text_input_rap, speaker, mode_input):
+ text_input_rap = f"({mode_input})" + text_input_rap
+ output_audio, sr = tts_engine(text_input_rap, speaker)
+ audio_type = "music"
+ music_path = save_audio(audio_type, output_audio, sr)
+ return music_path
+
+
+# 语音克隆
+def tts_clone(text, wav_file, speaker_prompt, emotion, language, speed):
+ clone_speaker = {
+ "wav_path": wav_file,
+ "speaker": "custom_voice",
+ "prompt_text": speaker_prompt,
+ }
+ clone_text = (
+ (f"({emotion})" if emotion else "")
+ + (f"({language})" if language else "")
+ + (f"({speed})" if speed else "")
+ + text
+ )
+ output_audio, sr = tts_engine(clone_text, "", clone_speaker)
+ audio_type = "clone"
+ clone_path = save_audio(audio_type, output_audio, sr)
+ return clone_path
+
+
+def launch_demo(args):
+ # 选项列表
+ emotion_options = ["高兴1", "高兴2", "生气1", "生气2", "悲伤1", "撒娇1"]
+ language_options = ["中文", "英文", "韩语", "日语", "四川话", "粤语", "广东话"]
+ speed_options = ["慢速1", "慢速2", "快速1", "快速2"]
+ speaker_options = ["Tingting"]
+ # Gradio 界面
+ with gr.Blocks() as demo:
+ gr.Markdown("## 🎙️ Step-Audio-TTS-3B Demo")
+
+ # 普通语音合成
+ with gr.Tab("Common TTS (普通语音合成)"):
+ text_input = gr.Textbox(
+ label="Input Text (输入文本)",
+ )
+ speaker_input = gr.Dropdown(
+ speaker_options,
+ label="Speaker Selection (音色选择)",
+ )
+ emotion_input = gr.Dropdown(
+ emotion_options,
+ label="Emotion Style (情感风格)",
+ allow_custom_value=True,
+ interactive=True,
+ )
+ language_input = gr.Dropdown(
+ language_options,
+ label="Language/Dialect (语言/方言)",
+ allow_custom_value=True,
+ interactive=True,
+ )
+ speed_input = gr.Dropdown(
+ speed_options,
+ label="Speech Rate (语速调节)",
+ allow_custom_value=True,
+ interactive=True,
+ )
+ submit_btn = gr.Button("🔊 Generate Speech (生成语音)")
+ output_audio = gr.Audio(
+ label="Output Audio (合成语音)",
+ interactive=False,
+ )
+
+ submit_btn.click(
+ tts_common,
+ inputs=[
+ text_input,
+ speaker_input,
+ emotion_input,
+ language_input,
+ speed_input,
+ ],
+ outputs=output_audio,
+ )
+
+ # RAP / 哼唱模式
+ with gr.Tab("RAP/Humming Mode (RAP/哼唱模式)"):
+ text_input_rap = gr.Textbox(
+ label="Lyrics Input (歌词输入)",
+ )
+ speaker_input = gr.Dropdown(
+ speaker_options,
+ label="Speaker Selection (音色选择)",
+ )
+ mode_input = gr.Radio(
+ ["RAP", "Humming (哼唱)"],
+ value="RAP",
+ label="Generation Mode (生成模式)",
+ )
+ submit_btn_rap = gr.Button("🎤 Generate Performance (生成演绎)")
+ output_audio_rap = gr.Audio(
+ label="Performance Audio (演绎音频)", interactive=False
+ )
+ submit_btn_rap.click(
+ tts_music,
+ inputs=[text_input_rap, speaker_input, mode_input],
+ outputs=output_audio_rap,
+ )
+
+ with gr.Tab("Voice Clone (语音克隆)"):
+ text_input_clone = gr.Textbox(
+ label="Target Text (目标文本)",
+ placeholder="Text to be synthesized with cloned voice (待克隆语音合成的文本)",
+ )
+ audio_input = gr.File(
+ label="Reference Audio Upload (参考音频上传)",
+ )
+ speaker_prompt = gr.Textbox(
+ label="Exact text from reference audio (输入参考音频的准确文本)",
+ )
+ emotion_input = gr.Dropdown(
+ emotion_options,
+ label="Emotion Style (情感风格)",
+ allow_custom_value=True,
+ interactive=True,
+ )
+ language_input = gr.Dropdown(
+ language_options,
+ label="Language/Dialect (语言/方言)",
+ allow_custom_value=True,
+ interactive=True,
+ )
+ speed_input = gr.Dropdown(
+ speed_options,
+ label="Speech Rate (语速调节)",
+ allow_custom_value=True,
+ interactive=True,
+ )
+ submit_btn_clone = gr.Button("🗣️ Synthesize Cloned Speech (合成克隆语音)")
+ output_audio_clone = gr.Audio(
+ label="Cloned Speech Output (克隆语音输出)",
+ interactive=False,
+ )
+ submit_btn_clone.click(
+ tts_clone,
+ inputs=[
+ text_input_clone,
+ audio_input,
+ speaker_prompt,
+ emotion_input,
+ language_input,
+ speed_input,
+ ],
+ outputs=output_audio_clone,
+ )
+
+ # 启动 Gradio demo
+ demo.queue().launch(server_name=args.server_name, server_port=args.server_port)
+
+
+if __name__ == "__main__":
+ # 解析命令行参数
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--model-path", type=str, required=True, help="Model path.")
+ parser.add_argument(
+ "--server-name", type=str, default="0.0.0.0", help="Demo server name."
+ )
+ parser.add_argument(
+ "--server-port", type=int, default=7860, help="Demo server port."
+ )
+ parser.add_argument("--tmp_dir", type=str, default="/tmp/gradio", help="Save path.")
+
+ args = parser.parse_args()
+ # 使用解析后的命令行参数设置模型路径
+ model_path = args.model_path
+ encoder = StepAudioTokenizer(os.path.join(model_path, "Step-Audio-Tokenizer"))
+ tts_engine = StepAudioTTS(os.path.join(model_path, "Step-Audio-TTS-3B"), encoder)
+ launch_demo(args)
diff --git a/almeval/models/stepaudio/utils.py b/almeval/models/stepaudio/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f35d7a19e908ff65a2bddcbadfefb77cae443d7
--- /dev/null
+++ b/almeval/models/stepaudio/utils.py
@@ -0,0 +1,185 @@
+import io
+import base64
+
+import librosa
+import numpy as np
+import math
+import os
+import threading
+import torch
+import torchaudio
+import sox
+import tempfile
+
+
+def encode_wav(wav, sr, rep_format="wav"):
+ with io.BytesIO() as wavio:
+ torchaudio.save(wavio, wav, sr, format=rep_format)
+ audio_bytes = wavio.getvalue()
+ encoded_wav = base64.b64encode(audio_bytes).decode("ascii")
+ return encoded_wav
+
+
+def trim_silence(audio, sr, keep_left_time=0.05, keep_right_time=0.22, hop_size=240):
+ _, index = librosa.effects.trim(audio, top_db=20, frame_length=512, hop_length=128)
+ num_frames = int(math.ceil((index[1] - index[0]) / hop_size)) # 300
+
+ left_sil_samples = int(keep_left_time * sr)
+ right_sil_samples = int(keep_right_time * sr)
+
+ wav_len = len(audio)
+ start_idx = index[0] - left_sil_samples
+ trim_wav = audio
+
+ if start_idx > 0:
+ trim_wav = trim_wav[start_idx:]
+ else:
+ trim_wav = np.pad(
+ trim_wav, (abs(start_idx), 0), mode="constant", constant_values=0.0
+ )
+ wav_len = len(trim_wav)
+ out_len = int(num_frames * hop_size + (keep_left_time + keep_right_time) * sr)
+
+ if out_len < wav_len:
+ trim_wav = trim_wav[:out_len]
+ else:
+ trim_wav = np.pad(
+ trim_wav, (0, (out_len - wav_len)), mode="constant", constant_values=0.0
+ )
+ return trim_wav
+
+
+def volumn_adjust(audio16bit_torch, sr, volumn_ratio):
+ """使用sox进行音频音量调整
+ Args:
+ audio16bit_torch (Tensor): 输入音频张量 [1, samples]
+ volume_ratio (float): 音量比率,>1增大音量,<1降低音量
+
+ Returns:
+ Tensor: 调整音量后的音频张量
+ """
+ # 创建临时文件
+ with tempfile.NamedTemporaryFile(
+ suffix=".wav", delete=True
+ ) as temp_in, tempfile.NamedTemporaryFile(suffix=".wav", delete=True) as temp_out:
+ # 保存输入音频到临时文件
+ torchaudio.save(temp_in.name, audio16bit_torch, sr) # 假设采样率为16000
+ # 创建sox转换器
+ tfm = sox.Transformer()
+ tfm.vol(volumn_ratio) # 设置音量调整比率
+ # 应用音量调整
+ tfm.build_file(temp_in.name, temp_out.name)
+ # 读取处理后的音频
+ audio_changed, _ = torchaudio.load(temp_out.name)
+ return audio_changed
+
+
+def speech_adjust(audio16bit_torch, sr, speed_ratio):
+ """使用sox进行音频变速处理
+ Args:
+ audio16bit_torch (Tensor): 输入音频张量 [1, samples]
+ speed_ratio (float): 速度比率,>1加速,<1减速
+
+ Returns:
+ Tensor: 变速后的音频张量
+ """
+ # 创建临时文件
+ with tempfile.NamedTemporaryFile(
+ suffix=".wav", delete=True
+ ) as temp_in, tempfile.NamedTemporaryFile(suffix=".wav", delete=True) as temp_out:
+ # 保存输入音频到临时文件
+ torchaudio.save(temp_in.name, audio16bit_torch, sr) # 假设采样率为16000
+ # 创建sox转换器
+ tfm = sox.Transformer()
+ tfm.tempo(speed_ratio) # 设置变速比率
+ # 应用变速处理
+ tfm.build_file(temp_in.name, temp_out.name)
+ # 读取处理后的音频
+ audio_changed, _ = torchaudio.load(temp_out.name)
+ return audio_changed
+
+
+def audio_resample(audio16bit_torch, result_sr, target_sample_rate):
+ audio16bit_torch = torchaudio.transforms.Resample(
+ orig_freq=result_sr, new_freq=target_sample_rate
+ )(audio16bit_torch)
+ result_sr = target_sample_rate
+ return audio16bit_torch, result_sr
+
+
+def norm_audio(audio16bit_torch):
+ # 直接 归一化处理。
+ audio16bit_torch = audio16bit_torch.numpy()
+ audio16bit_torch = (
+ audio16bit_torch / np.abs(audio16bit_torch).max() * 32767
+ ).astype(np.int16)
+ audio16bit_torch = torch.from_numpy(audio16bit_torch)
+ return audio16bit_torch
+
+
+def resample_audio(wav, original_sample_rate, target_sample_rate):
+ if original_sample_rate != target_sample_rate:
+ assert (
+ original_sample_rate > target_sample_rate
+ ), "wav sample rate {} must be greater than {}".format(
+ original_sample_rate, target_sample_rate
+ )
+ wav = torchaudio.transforms.Resample(
+ orig_freq=original_sample_rate, new_freq=target_sample_rate
+ )(wav)
+ return wav
+
+
+def energy_norm_fn(wav):
+ if type(wav) is np.ndarray:
+ max_data = np.max(np.abs(wav))
+ wav = wav / max(max_data, 0.01) * 0.999
+ else:
+ max_data = torch.max(torch.abs(wav))
+ wav = wav / max(max_data, 0.01) * 0.999
+ return wav
+
+
+def get_audio_tokens(audio_tokens: str) -> list[int]:
+ audio_tokens = audio_tokens.split(">", "")) + 65536
+ for token in audio_tokens
+ ]
+ return audio_tokens
+
+
+def load_audio(audio_path: str):
+ audio_wav, sr = torchaudio.load(audio_path)
+ audio_wav = audio_wav.mean(dim=0, keepdim=True)
+ return audio_wav, sr
+
+
+# load optimus_ths for flash attention, make sure LD_LIBRARY_PATH has `nvidia/cuda_nvrtc/lib`
+# if not, please manually set LD_LIBRARY_PATH=xxx/python3.10/site-packages/nvidia/cuda_nvrtc/lib
+def load_optimus_ths_lib(libpath):
+ if not hasattr(load_optimus_ths_lib, 'lock'):
+ load_optimus_ths_lib.lock = threading.Lock()
+ if not hasattr(load_optimus_ths_lib, 'success'):
+ load_optimus_ths_lib.success = False
+
+ with load_optimus_ths_lib.lock:
+ if load_optimus_ths_lib.success:
+ return load_optimus_ths_lib.success
+
+ try:
+ if torch.__version__ >= "2.5":
+ torch.ops.load_library(os.path.join(libpath, 'liboptimus_ths-torch2.5-cu124.cpython-310-x86_64-linux-gnu.so'))
+ elif torch.__version__ >= "2.3":
+ torch.ops.load_library(os.path.join(libpath, 'liboptimus_ths-torch2.3-cu121.cpython-310-x86_64-linux-gnu.so'))
+ elif torch.__version__ >= "2.2":
+ torch.ops.load_library(os.path.join(libpath, 'liboptimus_ths-torch2.2-cu121.cpython-310-x86_64-linux-gnu.so'))
+ else:
+ raise RuntimeError("Unsupported torch version")
+ print("Load optimus_ths successfully and flash attn would be enabled")
+ load_optimus_ths_lib.success = True
+ except Exception as err:
+ print(f"Fail to load optimus_ths and flash attn is disabled: {err}")
+ load_optimus_ths_lib.success = False
+
+ return load_optimus_ths_lib.success
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