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---
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license: mit
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language:
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- en
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- zh
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base_model:
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- CosyVoice3
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pipeline_tag: text-to-speech
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library_name: transformers
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tags:
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- CosyVoice3
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- Speech
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---
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# CosyVoice3
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This version of CosyVoice3 has been converted to run on the Axera NPU using **w8a16** quantization.
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Compatible with Pulsar2 version: 4.2
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## Convert tools links:
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For those who are interested in model conversion, you can try to export axmodel through the original repo :
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[Cosyvoice](https://github.com/FunAudioLLM/CosyVoice)
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[Pulsar2 Link, How to Convert LLM from Huggingface to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/appendix/build_llm.html)
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[AXera NPU HOST LLM Runtime](https://github.com/AXERA-TECH/CosyVoice3.Axera)
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## Support Platform
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- AX650
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- AX650N DEMO Board
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- [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
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- [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html)
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**Speech Generation**
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| Stage | Time |
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|------|------|
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| llm prefill ( input_token_num + prompt_token_num 在 [0,128 ] ) | 104 ms |
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| llm prefill ( input_token_num + prompt_token_num 在 [128,256 ] ) | 160 ms |
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| Decode | 14 token/s |
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## How to use
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Download all files from this repository to the device
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### 1. PrePare
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#### 1.1 Copy this project to AX650 Board
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#### 1.2 Prepare Dependencies
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**Running HTTP Tokenizer Server** and **Processing Prompt Speech** require these Python packages. If you run these two step on a PC, install them on the PC.
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```
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pip3 install -r scripts/requirements.txt
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```
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### 2. Start HTTP Tokenizer Server
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```
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cd scripts
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python scripts/cosyvoice3_tokenizer.py --host {your host} --port {your port}
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```
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### 3. Run on Axera Device
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There are 2 kinds of device, AX650 Board , AXCL aarch64 Board and AXCL x86 Board.
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#### 3.1 Run on AX650 Board
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1) Moidfy the HTTP host in `run_ax650.sh`.
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2) Run `run_ax650.sh`
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```shell
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root@ax650 ~/CosyVoice3 # bash run_ax650.sh
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rm: cannot remove 'output*.wav': No such file or directory
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[I][ Init][ 108]: LLM init start
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[I][ Init][ 34]: connect http://10.122.86.184:12345 ok
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bos_id: 0, eos_id: 1773
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7% | ███ | 2 / 27 [3.11s<42.04s, 0.64 count/s] embed_selector init ok[I][ Init][ 138]: attr.axmodel_num:24
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100% | ████████████████████████████████ | 27 / 27 [10.32s<10.32s, 2.62 count/s] init post axmodel ok,remain_cmm(7178 MB)
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[I][ Init][ 216]: max_token_len : 1023
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[I][ Init][ 221]: kv_cache_size : 128, kv_cache_num: 1023
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[I][ Init][ 229]: prefill_token_num : 128
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[I][ Init][ 233]: grp: 1, prefill_max_token_num : 1
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[I][ Init][ 233]: grp: 2, prefill_max_token_num : 128
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[I][ Init][ 233]: grp: 3, prefill_max_token_num : 256
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[I][ Init][ 233]: grp: 4, prefill_max_token_num : 384
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[I][ Init][ 233]: grp: 5, prefill_max_token_num : 512
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[I][ Init][ 237]: prefill_max_token_num : 512
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[I][ Init][ 249]: LLM init ok
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[I][ Init][ 154]: Token2Wav init ok
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[I][ main][ 273]:
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[I][ Run][ 388]: input token num : 142, prefill_split_num : 2
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[I][ Run][ 422]: input_num_token:128
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[I][ Run][ 422]: input_num_token:14
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[I][ Run][ 607]: ttft: 236.90 ms
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[Main/Token2Wav Thread] Processing batch of 28 tokens...
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Successfully saved audio to output_0.wav (32-bit Float PCM).
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[Main/Token2Wav Thread] Processing batch of 53 tokens...
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Successfully saved audio to output_1.wav (32-bit Float PCM).
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[Main/Token2Wav Thread] Processing batch of 78 tokens...
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Successfully saved audio to output_2.wav (32-bit Float PCM).
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[Main/Token2Wav Thread] Processing batch of 78 tokens...
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Successfully saved audio to output_3.wav (32-bit Float PCM).
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[Main/Token2Wav Thread] Processing batch of 78 tokens...
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Successfully saved audio to output_4.wav (32-bit Float PCM).
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[Main/Token2Wav Thread] Processing batch of 78 tokens...
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Successfully saved audio to output_5.wav (32-bit Float PCM).
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[Main/Token2Wav Thread] Processing batch of 78 tokens...
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Successfully saved audio to output_6.wav (32-bit Float PCM).
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[Main/Token2Wav Thread] Processing batch of 78 tokens...
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Successfully saved audio to output_7.wav (32-bit Float PCM).
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[Main/Token2Wav Thread] Processing batch of 78 tokens...
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Successfully saved audio to output_8.wav (32-bit Float PCM).
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[Main/Token2Wav Thread] Processing batch of 78 tokens...
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Successfully saved audio to output_9.wav (32-bit Float PCM).
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[I][ Run][ 723]: hit eos, llm finished
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[I][ Run][ 753]: llm finished
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[Main/Token2Wav Thread] Buffer is empty and LLM finished. Exiting.
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[I][ Run][ 758]: total decode tokens:271
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[N][ Run][ 759]: hit eos,avg 21.47 token/s
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Successfully saved audio to output_10.wav (32-bit Float PCM).
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Successfully saved audio to output.wav (32-bit Float PCM).
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Voice generation pipeline completed.
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Type "q" to exit, Ctrl+c to stop current running
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text >>
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```
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Output Speech：
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[output.wav](asset/output.wav)
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#### Or run on AX650 Board with Gradio GUI
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1) Start server
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```
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bash run_api_ax650.sh
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```
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2) Start Gradio GUI
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```
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python scripts/gradio_demo.py
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```
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#### 3.2 Run on AXCL aarch64 Board
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```
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bash run_axcl_aarch64.sh
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```
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#### Or run on AXCL aarch64 Board with Gradio GUI
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1) Start server
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```
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bash run_api_axcl_aarch64.sh
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```
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2) Start Gradio GUI
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```
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python scripts/gradio_demo.py
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```
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3) Open the page from a browser
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The page url is : `https://{your device ip}:7860`
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Note that you need to run these two commands in the project root directory.
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#### 3.3 Run on AXCL x86 Board
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```
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bash run_axcl_x86.sh
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```
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#### Or run on AXCL aarch64 Board with Gradio GUI
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1) Start server
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```
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bash run_api_axcl_x86.sh
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```
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2) Start Gradio GUI
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```
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python scripts/gradio_demo.py
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```
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3) Open the page from a browser
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The page url is : `https://{your device ip}:7860`
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Note that you need to run these two commands in the project root directory.
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### Optional. Process Prompt Speech
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If you want to replicate a specific sound, do this step.
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You can use audio in asset/ .
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#### (1). Downlaod wetext
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```
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pip3 install modelscope
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modelscope download --model pengzhendong/wetext --local_dir pengzhendong/wetext
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```
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#### (2). Process Prompt Speech
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Example:
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```
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python3 scripts/process_prompt.py --prompt_text asset/zh_man1.txt --prompt_speech asset/zh_man1.wav --output zh_man1
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```
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Pass parameters according to the actual situation.
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```
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python3 scripts/process_prompt.py -h
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usage: process_prompt.py [-h] [--model_dir MODEL_DIR] [--wetext_dir WETEXT_DIR] [--sample_rate SAMPLE_RATE] [--prompt_text PROMPT_TEXT] [--prompt_speech PROMPT_SPEECH]
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[--output OUTPUT]
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options:
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-h, --help show this help message and exit
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--model_dir MODEL_DIR
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tokenizer configuration directionary
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--wetext_dir WETEXT_DIR
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path to wetext
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--sample_rate SAMPLE_RATE
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Sampling rate for prompt audio
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--prompt_text PROMPT_TEXT
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The text content of the prompt(reference) audio. Text or file path.
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--prompt_speech PROMPT_SPEECH
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The path to prompt(reference) audio.
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--output OUTPUT Output data storage directory
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```
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After executing the above command, files like the following will be generated:
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```
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flow_embedding.txt
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flow_prompt_speech_token.txt
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llm_embedding.txt
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llm_prompt_speech_token.txt
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prompt_speech_feat.txt
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prompt_text.txt
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```
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When you run run_ax650.sh, pass the output path here to the prompt_files parameter of the run_ax650.sh script.
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