SenseVoice (Sensevoice_Api)

SenseVoice/Sensevoice_Api/.gitattributes

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SenseVoice/Sensevoice_Api/Dockerfile

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+FROM python:3.8-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+COPY main.py .
+COPY iic iic/
+
+RUN pip install --upgrade pip
+RUN pip install torch>=1.13 torchaudio --index-url https://download.pytorch.org/whl/cpu
+RUN pip install -r requirements.txt
+
+CMD ["python", "main.py"]

SenseVoice/Sensevoice_Api/README.md

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+# SenseVoice-Api
+此项目是基于SenseVoice的funasr_onnx版本进行的api发布, 使用Python3.10.14开发
+
+# SenseVoice
+SenseVoice是具有音频理解能力的音频基础模型，包括语音识别（ASR）、语种识别（LID）、语音情感识别（SER）和声学事件分类（AEC）或声学事件检测（AED）。本项目提供SenseVoice模型的介绍以及在多个任务测试集上的benchmark，以及体验模型所需的环境安装的与推理方式。
+
+<a name="核心功能"></a>
+# 核心功能 🎯
+**SenseVoice**专注于高精度多语言语音识别、情感辨识和音频事件检测
+- **多语言识别：** 采用超过40万小时数据训练，支持超过50种语言，识别效果上优于Whisper模型。
+- **富文本识别：** 
+  - 具备优秀的情感识别，能够在测试数据上达到和超过目前最佳情感识别模型的效果。
+  - 支持声音事件检测能力，支持音乐、掌声、笑声、哭声、咳嗽、喷嚏等多种常见人机交互事件进行检测。
+- **高效推理：** SenseVoice-Small模型采用非自回归端到端框架，推理延迟极低，10s音频推理仅耗时70ms，15倍优于Whisper-Large。
+- **微调定制：** 具备便捷的微调脚本与策略，方便用户根据业务场景修复长尾样本问题。
+- **服务部署：** 具有完整的服务部署链路，支持多并发请求，支持客户端语言有，python、c++、html、java与c#等。
+
+### Docker部署(CPU+量化模型)
+```
+# 国内
+docker pull registry.cn-hangzhou.aliyuncs.com/yiminger/sensevoice:latest
+docker run -p 8000:8000 registry.cn-hangzhou.aliyuncs.com/yiminger/sensevoice:latest
+
+# Docker hub
+docker pull yiminger/sensevoice:latest
+# 运行
+docker run -p 8000:8000 yiminger/sensevoice:latest
+```
+
+### 本地安装
+```
+git clone https://github.com/HG-ha/SenseVoice-Api.git && cd SenseVoice-Api
+# 安装依赖
+pip install -r requirements.txt
+# 运行
+python main.py
+```
+
+### 接口测试
+1. 从URL转文字
+   ```
+   curl --location --request POST 'http://127.0.0.1:8000/extract_text' \
+    --form 'url=https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav'
+   ```
+2. 从文件转文字
+   ```
+   curl --request POST \
+    --url http://127.0.0.1:8000/extract_text \
+    --header 'content-type: multipart/form-data' \
+    --form 'file=@asr_example_zh.wav'
+   ```
+
+### 接口文档
+```
+http://127.0.0.1:8000/docs
+```

SenseVoice/Sensevoice_Api/iic/SenseVoiceSmall/am.mvn

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SenseVoice/Sensevoice_Api/iic/SenseVoiceSmall/chn_jpn_yue_eng_ko_spectok.bpe.model

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+oid sha256:aa87f86064c3730d799ddf7af3c04659151102cba548bce325cf06ba4da4e6a8
+size 377341

SenseVoice/Sensevoice_Api/iic/SenseVoiceSmall/config.yaml

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+encoder: SenseVoiceEncoderSmall
+encoder_conf:
+    output_size: 512
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 50
+    tp_blocks: 20
+    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
+
+
+model: SenseVoiceSmall
+model_conf:
+    length_normalized_loss: true
+    sos: 1
+    eos: 2
+    ignore_id: -1
+
+tokenizer: SentencepiecesTokenizer
+tokenizer_conf:
+  bpemodel: null
+  unk_symbol: <unk>
+  split_with_space: true
+
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 7
+    lfr_n: 6
+    cmvn_file: null
+
+
+dataset: SenseVoiceCTCDataset
+dataset_conf:
+  index_ds: IndexDSJsonl
+  batch_sampler: EspnetStyleBatchSampler
+  data_split_num: 32
+  batch_type: token
+  batch_size: 14000
+  max_token_length: 2000
+  min_token_length: 60
+  max_source_length: 2000
+  min_source_length: 60
+  max_target_length: 200
+  min_target_length: 0
+  shuffle: true
+  num_workers: 4
+  sos: ${model_conf.sos}
+  eos: ${model_conf.eos}
+  IndexDSJsonl: IndexDSJsonl
+  retry: 20
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 20
+  keep_nbest_models: 10
+  avg_nbest_model: 10
+  log_interval: 100
+  resume: true
+  validate_interval: 10000
+  save_checkpoint_interval: 10000
+
+optim: adamw
+optim_conf:
+  lr: 0.00002
+scheduler: warmuplr
+scheduler_conf:
+  warmup_steps: 25000
+
+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

SenseVoice/Sensevoice_Api/iic/SenseVoiceSmall/model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ecaedf07c74e48ee3481204453f03f7d8a383ee226d136dece265e94457af660
+size 937615371

SenseVoice/Sensevoice_Api/iic/SenseVoiceSmall/model_quant.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b466b19006784340a9f09af96f37778363ccc50917db02d4dc10ca260d73434c
+size 241217542

SenseVoice/Sensevoice_Api/main.py

@@ -0,0 +1,120 @@
+# -*- coding: utf-8 -*-
+"""
+Author: 一铭
+Date  : 2024-08-28
+
+Github: https://github.com/HG-ha
+Home  : https://api2.wer.plus
+
+Description:
+    From ali dharma school project: https://github.com/FunAudioLLM/SenseVoice
+
+    This program is distributed using ONNX-encapsulated fastapi,Provides an interface for reading audio from a network or file and predicting content.
+
+    If you need to use cuda, you need to install the OnnxRun-time gpu, not the onnxruntime.
+"""
+
+import librosa
+import numpy as np
+import aiohttp
+from fastapi import FastAPI, Form, UploadFile, HTTPException
+from pydantic import HttpUrl, ValidationError, BaseModel, Field
+from typing import List, Union
+from funasr_onnx import SenseVoiceSmall
+from funasr_onnx.utils.postprocess_utils import rich_transcription_postprocess
+from io import BytesIO
+
+
+class ApiResponse(BaseModel):
+    message: str = Field(..., description="Status message indicating the success of the operation.")
+    results: str = Field(..., description="Remove label output")
+    label_result: str = Field(..., description="Default output")
+
+
+app = FastAPI()
+
+async def from_url_load_audio(audio: HttpUrl) -> np.array:
+    async with aiohttp.ClientSession() as session:
+        async with session.get(
+            audio,
+            headers={
+                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36 Edg/127.0.0.0"
+            },
+        ) as response:
+            if response.status != 200:
+                raise HTTPException(
+                    status_code=400,
+                    detail=f"Failed to download image: {response.status}",
+                )
+            image_bytes = await response.read()
+            return BytesIO(image_bytes)
+
+@app.post("/extract_text",response_model=ApiResponse)
+async def upload_url(url: Union[HttpUrl, None] = Form(None), file: Union[UploadFile, None] = Form(None)):
+    if file:
+        audio = BytesIO(await file.read())
+    elif url:
+        try:
+            audio = await from_url_load_audio(str(url))
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+
+    else:
+        return HTTPException(400,{"error": "No valid audio source provided."})
+    try:
+        res = model(audio, language=language, use_itn=True)
+        return {
+            "message": "input processed successfully", 
+            "results": rich_transcription_postprocess(res[0]),
+            "label_result": res[0]
+            }
+    except ValidationError as e:
+        raise HTTPException(status_code=400, detail=e.errors())
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+if __name__ == "__main__":
+
+    model_dir = "iic/SenseVoiceSmall"
+    device_id = 0  # Use GPU 0, automatically use CPU when not available
+    batch_size = 16
+    language = "auto"
+    quantize = True # Quantization model, small size, fast speed, accuracy may be insufficient: model_quant.onnx
+    # quantize = False # Standard model: model.onnx
+
+    # Override built-in load_data method to fix np.ndarray type accuracy bug
+    # cannot pass the librosa.load object directly, which would make the accuracy of other languages extremely poor
+    # No specific reason
+    def load_data(self, wav_content: Union[str, np.ndarray, List[str], BytesIO], fs: int = None) -> List:
+        def load_wav(path: str) -> np.ndarray:
+            waveform, _ = librosa.load(path, sr=fs)
+            return waveform
+
+        if isinstance(wav_content, np.ndarray):
+            return [wav_content]
+
+        if isinstance(wav_content, str):
+            return [load_wav(wav_content)]
+
+        if isinstance(wav_content, list):
+            return [load_wav(path) for path in wav_content]
+        
+        if isinstance(wav_content, BytesIO):
+            return [load_wav(wav_content)]
+        
+        raise TypeError(f"The type of {wav_content} is not in [str, np.ndarray, list]")
+    
+    SenseVoiceSmall.load_data = load_data
+
+    model = SenseVoiceSmall(
+        model_dir,
+        quantize=quantize,
+        device_id=device_id,
+        batch_size=batch_size
+        )
+
+    print("\n\nDocs: http://127.0.0.1:8000/docs\n")
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)

SenseVoice/Sensevoice_Api/requirements.txt

@@ -0,0 +1,8 @@
+funasr_onnx==0.4.1
+fastapi==0.112.2
+numpy==1.24.4
+uvicorn==0.30.6
+librosa==0.10.2
+aiohttp==3.10.5
+python-multipart==0.0.9
+jieba==0.42.1

SenseVoice/Sensevoice_Api/source.txt

@@ -0,0 +1 @@
+https://huggingface.co/mingl/Sensevoice_Api