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.gitattributes

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+speech_tokenizer_v1.onnx filter=lfs diff=lfs merge=lfs -text
+campplus.onnx filter=lfs diff=lfs merge=lfs -text
+flow.pt filter=lfs diff=lfs merge=lfs -text
+hift.pt filter=lfs diff=lfs merge=lfs -text
+llm.pt filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,51 +1,97 @@
----
-frameworks:
-- Pytorch
-license: Apache License 2.0
-tasks:
-- text-to-speech
+# CosyVoice
 
-#model-type:
-##如 gpt、phi、llama、chatglm、baichuan 等
-#- gpt
+## Install
 
-#domain:
-##如 nlp、cv、audio、multi-modal
-#- nlp
+**Clone and install**
 
-#language:
-##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
-#- cn 
+- Clone the repo
+``` sh
+git clone https://github.com/modelscope/cosyvoice.git
+```
+
+- Install Conda: please see https://docs.conda.io/en/latest/miniconda.html
+- Create Conda env:
+
+``` sh
+conda create -n cosyvoice python=3.8
+conda activate cosyvoice
+pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com
+```
 
-#metrics:
-##如 CIDEr、Blue、ROUGE 等
-#- CIDEr
+**Model download**
 
-#tags:
-##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
-#- pretrained
+We strongly recommand that you download our pretrained multi_lingual and mutli_emotion model.
 
-#tools:
-##如 vllm、fastchat、llamacpp、AdaSeq 等
-#- vllm
----
-### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
-#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
+If you are expert in this field, and you are only interested in training your own CosyVoice model from scratch, you can skip this step.
 
-SDK下载
-```bash
-#安装ModelScope
-pip install modelscope
+``` sh
+mkdir -p pretrained_models
+git clone https://www.modelscope.cn/CosyVoice/multi_lingual_cosytts.git pretrained_models/multi_lingual_cosytts
+git clone https://www.modelscope.cn/CosyVoice/multi_emotion_cosytts.git pretrained_models/multi_emotion_cosytts
 ```
-```python
-#SDK模型下载
-from modelscope import snapshot_download
-model_dir = snapshot_download('speech_tts/CosyVoice-300M-Instruct')
+
+**Basic Usage**
+
+For zero_shot and sft inference, please use models in `pretrained_models/multi_lingual_cosytts`
+
 ```
-Git下载
+from cosyvoice.cli.cosyvoice import CosyVoice
+from cosyvoice.utils.file_utils import load_wav
+import torchaudio
+
+cosyvoice = CosyVoice('pretrained_models/multi_lingual_cosytts')
+
+# sft usage
+print(cosyvoice.list_avaliable_spks())
+output = cosyvoice.inference_sft('hello, my name is Jack. What is your name?', 'aishuo')
+torchaudio.save('sft.wav', output['tts_speech'], 22050)
+
+# zero_shot usage
+prompt_speech_22050 = load_wav('1089_134686_000002_000000.wav', 22050)
+output = cosyvoice.inference_zero_shot('hello, my name is Jack. What is your name?', 'It would be a gloomy secret night.', prompt_speech_22050)
+torchaudio.save('zero_shot.wav', output['tts_speech'], 22050)
 ```
-#Git模型下载
-git clone https://www.modelscope.cn/speech_tts/CosyVoice-300M-Instruct.git
+
+For instruct inference, please use models in `pretrained_models/multi_emotion_cosytts`
+
 ```
+from cosyvoice.cli.cosyvoice import CosyVoice
+from cosyvoice.utils.file_utils import load_wav
+import torchaudio
+
+cosyvoice = CosyVoice('pretrained_models/multi_emotion_cosytts')
+
+# instruct usage
+prompt_speech_22050 = load_wav('1089_134686_000002_000000.wav', 22050)
+output = cosyvoice.inference_instruct('hello, my name is Jack. What is your name?', 'It would be a gloomy secret night.', prompt_speech_22050, 'A serene woman articulates thoughtfully in a high pitch and slow tempo, exuding a peaceful and joyful aura.')
+torchaudio.save('instruct.wav', output['tts_speech'], 22050)
+```
+
+**Advanced Usage**
+
+For advanced user, we have provided train and inference scripts in `examples/libritts/cosyvoice/run.sh`.
+You can get familiar with CosyVoice following this recipie.
+
+**Start web demo**
+
+You can use our web demo page to get familiar with CosyVoice quickly.
+We only support zero_shot/sft inference in web demo.
+
+Please see the demo website for details.
+
+```
+python3 webui.py --port 50000 --model_dir pretrained_models/multi_lingual_cosytts
+```
+
+**Build for deployment**
+
+Optionally, if you want to use grpc for service deployment,
+you can run following steps. Otherwise, you can just ignore this step.
 
-<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
+``` sh
+cd runtime/python
+docker build -t cosyvoice:v1.0 .
+# change multi_lingual_cosytts to multi_emotion_cosytts if you want to use instruct inference
+docker run -d --runtime=nvidia -v `pwd`/../../pretrained_models/multi_lingual_cosytts:/opt/cosyvoice/cosyvoice/runtime/pretrained_models -p 50000:50000 cosyvoice:v1.0
+python3 client.py --port 50000 --mode <sft|zero_shot|instruct>
+```

campplus.onnx

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+size 28303423

cosyvoice.yaml

@@ -0,0 +1,196 @@
+# set random seed, so that you may reproduce your result.
+__set_seed1: !apply:random.seed [1986]
+__set_seed2: !apply:numpy.random.seed [1986]
+__set_seed3: !apply:torch.manual_seed [1986]
+__set_seed4: !apply:torch.cuda.manual_seed_all [1986]
+
+# fixed params
+sample_rate: 22050
+text_encoder_input_size: 512
+llm_input_size: 1024
+llm_output_size: 1024
+spk_embed_dim: 192
+
+# model params
+# for all class/function included in this repo, we use !<name> or !<new> for intialization, so that user may find all corresponding class/function according to one single yaml.
+# for system/third_party class/function, we do not require this.
+llm: !new:cosyvoice.llm.llm.TransformerLM
+    text_encoder_input_size: !ref <text_encoder_input_size>
+    llm_input_size: !ref <llm_input_size>
+    llm_output_size: !ref <llm_output_size>
+    text_token_size: 51866
+    speech_token_size: 4096
+    length_normalized_loss: True
+    lsm_weight: 0
+    spk_embed_dim: !ref <spk_embed_dim>
+    text_encoder: !new:cosyvoice.transformer.encoder.ConformerEncoder
+        input_size: !ref <text_encoder_input_size>
+        output_size: 1024
+        attention_heads: 16
+        linear_units: 4096
+        num_blocks: 6
+        dropout_rate: 0.1
+        positional_dropout_rate: 0.1
+        attention_dropout_rate: 0
+        normalize_before: True
+        input_layer: 'linear'
+        pos_enc_layer_type: 'rel_pos_espnet'
+        selfattention_layer_type: 'rel_selfattn'
+        use_cnn_module: False
+        macaron_style: False
+        use_dynamic_chunk: False
+        use_dynamic_left_chunk: False
+        static_chunk_size: 1
+    llm: !new:cosyvoice.transformer.encoder.TransformerEncoder
+        input_size: !ref <llm_input_size>
+        output_size: !ref <llm_output_size>
+        attention_heads: 16
+        linear_units: 4096
+        num_blocks: 14
+        dropout_rate: 0.1
+        positional_dropout_rate: 0.1
+        attention_dropout_rate: 0
+        input_layer: 'linear_legacy'
+        pos_enc_layer_type: 'rel_pos_espnet'
+        selfattention_layer_type: 'rel_selfattn'
+        static_chunk_size: 1
+
+flow: !new:cosyvoice.flow.flow.MaskedDiffWithXvec
+    input_size: 512
+    output_size: 80
+    spk_embed_dim: !ref <spk_embed_dim>
+    output_type: 'mel'
+    vocab_size: 4096
+    input_frame_rate: 50
+    only_mask_loss: True
+    encoder: !new:cosyvoice.transformer.encoder.ConformerEncoder
+        output_size: 512
+        attention_heads: 8
+        linear_units: 2048
+        num_blocks: 6
+        dropout_rate: 0.1
+        positional_dropout_rate: 0.1
+        attention_dropout_rate: 0.1
+        normalize_before: True
+        input_layer: 'linear'
+        pos_enc_layer_type: 'rel_pos_espnet'
+        selfattention_layer_type: 'rel_selfattn'
+        input_size: 512
+        use_cnn_module: False
+        macaron_style: False
+    length_regulator: !new:cosyvoice.flow.length_regulator.InterpolateRegulator
+        channels: 80
+        sampling_ratios: [1, 1, 1, 1]
+    decoder: !new:cosyvoice.flow.flow_matching.ConditionalCFM
+        in_channels: 240
+        n_spks: 1
+        spk_emb_dim: 80
+        cfm_params: !new:omegaconf.DictConfig
+            content:
+                sigma_min: 1e-06
+                solver: 'euler'
+                t_scheduler: 'cosine'
+                training_cfg_rate: 0.2
+                inference_cfg_rate: 0.7
+                reg_loss_type: 'l1'
+        estimator: !new:cosyvoice.flow.decoder.ConditionalDecoder
+            in_channels: 320
+            out_channels: 80
+            channels: [256, 256]
+            dropout: 0
+            attention_head_dim: 64
+            n_blocks: 4
+            num_mid_blocks: 12
+            num_heads: 8
+            act_fn: 'gelu'
+
+hift: !new:cosyvoice.hifigan.generator.HiFTGenerator
+    in_channels: 80
+    base_channels: 512
+    nb_harmonics: 8
+    sampling_rate: !ref <sample_rate>
+    nsf_alpha: 0.1
+    nsf_sigma: 0.003
+    nsf_voiced_threshold: 10
+    upsample_rates: [8, 8]
+    upsample_kernel_sizes: [16, 16]
+    istft_params:
+        n_fft: 16
+        hop_len: 4
+    resblock_kernel_sizes: [3, 7, 11]
+    resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
+    source_resblock_kernel_sizes: [7, 11]
+    source_resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5]]
+    lrelu_slope: 0.1
+    audio_limit: 0.99
+    f0_predictor: !new:cosyvoice.hifigan.f0_predictor.ConvRNNF0Predictor
+        num_class: 1
+        in_channels: 80
+        cond_channels: 512
+
+# processor functions
+parquet_opener: !name:cosyvoice.dataset.processor.parquet_opener
+get_tokenizer: !name:whisper.tokenizer.get_tokenizer
+    multilingual: True
+    num_languages: 100
+    language: 'en'
+    task: 'transcribe'
+tokenize: !name:cosyvoice.dataset.processor.tokenize
+    get_tokenizer: !ref <get_tokenizer>
+    allowed_special: 'all'
+filter: !name:cosyvoice.dataset.processor.filter
+    max_length: 40960
+    min_length: 0
+    token_max_length: 200
+    token_min_length: 1
+resample: !name:cosyvoice.dataset.processor.resample
+    resample_rate: !ref <sample_rate>
+feat_extractor: !name:matcha.utils.audio.mel_spectrogram
+    n_fft: 1024
+    num_mels: 80
+    sampling_rate: !ref <sample_rate>
+    hop_size: 256
+    win_size: 1024
+    fmin: 0
+    fmax: 8000
+    center: False
+compute_fbank: !name:cosyvoice.dataset.processor.compute_fbank
+    feat_extractor: !ref <feat_extractor>
+parse_embedding: !name:cosyvoice.dataset.processor.parse_embedding
+    normalize: True
+shuffle: !name:cosyvoice.dataset.processor.shuffle
+    shuffle_size: 1000
+sort: !name:cosyvoice.dataset.processor.sort
+    sort_size: 500  # sort_size should be less than shuffle_size
+batch: !name:cosyvoice.dataset.processor.batch
+    batch_type: 'dynamic'
+    max_frames_in_batch: 2000
+padding: !name:cosyvoice.dataset.processor.padding
+
+# dataset processor pipeline
+data_pipeline: [
+    !ref <parquet_opener>,
+    !ref <tokenize>,
+    !ref <filter>,
+    !ref <resample>,
+    !ref <compute_fbank>,
+    !ref <parse_embedding>,
+    !ref <shuffle>,
+    !ref <sort>,
+    !ref <batch>,
+    !ref <padding>,
+]
+
+# train conf
+train_conf:
+    optim: adam
+    optim_conf:
+        lr: 0.001
+    scheduler: warmuplr
+    scheduler_conf:
+        warmup_steps: 2500
+    max_epoch: 200
+    grad_clip: 5
+    accum_grad: 2
+    log_interval: 100
+    save_per_step: -1

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