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---
name: ❓ Questions/Help
about: If you have questions, please first search existing issues and docs
labels: 'question, needs triage'
---
Notice: In order to resolve issues more efficiently, please raise issue following the template.
（注意：为了更加高效率解决您遇到的问题，请按照模板提问，补充细节）
## ❓ Questions and Help
### Before asking:
1. search the issues.
2. search the docs.
<!-- If you still can't find what you need: -->
#### What is your question?
#### Code
<!-- Please paste a code snippet if your question requires it! -->
#### What have you tried?
#### What's your environment?
- OS (e.g., Linux):
- FunASR Version (e.g., 1.0.0):
- ModelScope Version (e.g., 1.11.0):
- PyTorch Version (e.g., 2.0.0):
- How you installed funasr (`pip`, source):
- Python version:
- GPU (e.g., V100M32)
- CUDA/cuDNN version (e.g., cuda11.7):
- Docker version (e.g., funasr-runtime-sdk-cpu-0.4.1)
- Any other relevant information:
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---
name: 🐛 Bug Report
about: Submit a bug report to help us improve
labels: 'bug, needs triage'
---
Notice: In order to resolve issues more efficiently, please raise issue following the template.
（注意：为了更加高效率解决您遇到的问题，请按照模板提问，补充细节）
## 🐛 Bug
<!-- A clear and concise description of what the bug is. -->
### To Reproduce
Steps to reproduce the behavior (**always include the command you ran**):
1. Run cmd '....'
2. See error
<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
#### Code sample
<!-- Ideally attach a minimal code sample to reproduce the decried issue.
Minimal means having the shortest code but still preserving the bug. -->
### Expected behavior
<!-- A clear and concise description of what you expected to happen. -->
### Environment
- OS (e.g., Linux):
- FunASR Version (e.g., 1.0.0):
- ModelScope Version (e.g., 1.11.0):
- PyTorch Version (e.g., 2.0.0):
- How you installed funasr (`pip`, source):
- Python version:
- GPU (e.g., V100M32)
- CUDA/cuDNN version (e.g., cuda11.7):
- Docker version (e.g., funasr-runtime-sdk-cpu-0.4.1)
- Any other relevant information:
### Additional context
<!-- Add any other context about the problem here. -->
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blank_issues_enabled: false
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---
name: 📚 Documentation/Typos
about: Report an issue related to documentation or a typo
labels: 'documentation, needs triage'
---
## 📚 Documentation
For typos and doc fixes, please go ahead and:
1. Create an issue.
2. Fix the typo.
3. Submit a PR.
Thanks!
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repos:
- repo: https://github.com/psf/black
rev: 24.4.0
hooks:
- id: black
args: ['--line-length=100'] # 示例参数，black默认使用4个空格缩进
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## Acknowledge
1. We borrowed a lot of code from [Kaldi](http://kaldi-asr.org/) for data preparation.
2. We borrowed a lot of code from [ESPnet](https://github.com/espnet/espnet). FunASR follows up the training and finetuning pipelines of ESPnet.
3. We referred [Wenet](https://github.com/wenet-e2e/wenet) for building dataloader for large scale data training.
4. We acknowledge [ChinaTelecom](https://github.com/zhuzizyf/damo-fsmn-vad-infer-httpserver) for contributing the VAD runtime.
5. We acknowledge [RapidAI](https://github.com/RapidAI) for contributing the Paraformer and CT_Transformer-punc runtime.
6. We acknowledge [AiHealthx](http://www.aihealthx.com/) for contributing the websocket service and html5.
7. We acknowledge [XVERSE](http://www.xverse.cn/index.html) for contributing the grpc service.
8. We acknowledge [blt](https://github.com/bltcn) for develop and deploy website.
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FunASR Model Open Source License
Version 1.0
Copyright (C) [2023-2028] Alibaba Group. All rights reserved.
Thank you for choosing the FunASR open source models. The FunASR open source models contain a series of open-source models that allow everyone to use, modify, share, and learn from it.
To ensure better community collaboration, we have developed the following agreement and hope that you carefully read and abide by it.
1 Definitions
In this agreement, [FunASR software] refers to the FunASR open source model, and its derivatives, including fine-tuned models. [You] refer to individuals or organizations who use, modify, share, and learn from [FunASR software].
2 License and Restrictions
2.1 License
You are free to use, copy, modify, and share [FunASR software] under the conditions of this agreement.
2.2 Restrictions
You should indicate the code and model source and author information when using, copying, modifying and sharing [FunASR software]. You should keep the relevant names of models in [FunASR software].
3 Responsibility and Risk
[FunASR software] is for reference and learning purposes only and is not responsible for any direct or indirect losses caused by your use or modification of [FunASR software]. You should take responsibility and risks for your use and modification of [FunASR software].
4 Termination
If you violate any terms of this agreement, your license will be automatically terminated, and you must stop using, copying, modifying, and sharing [FunASR software].
5 Revision
This agreement may be updated and revised from time to time. The revised agreement will be published in the FunASR official repository and automatically take effect. If you continue to use, copy, modify, and share [FunASR software], it means you agree to the revised agreement.
6 Other Provisions
This agreement is subject to the laws of [Country/Region]. If any provisions are found to be illegal, invalid, or unenforceable, they shall be deemed deleted from this agreement, and the remaining provisions shall remain valid and binding.
If you have any questions or comments about this agreement, please contact us.
Copyright (c) [2023-2028] Alibaba Group. All rights reserved.
FunASR 模型开源协议
版本号：1.0
版权所有 (C) [2023-2028] [阿里巴巴集团]。保留所有权利。
感谢您选择 FunASR 开源模型。FunASR 开源模型包含一系列免费且开源的工业模型，让大家可以使用、修改、分享和学习该模型。
为了保证更好的社区合作，我们制定了以下协议，希望您仔细阅读并遵守本协议。
1 定义
本协议中，[FunASR 软件]指 FunASR 开源模型权重及其衍生品，包括 Finetune 后的模型；[您]指使用、修改、分享和学习[FunASR 软件]的个人或组织。
2 许可和限制
2.1 许可
您可以在遵守本协议的前提下，自由地使用、复制、修改和分享[FunASR 软件]。
2.2 限制
您在使用、复制、修改和分享[FunASR 软件]时，必须注明出处以及作者信息，并保留[FunASR 软件]中相关模型名称。
3 责任和风险承担
[FunASR 软件]仅作为参考和学习使用，不对您使用或修改[FunASR 软件]造成的任何直接或间接损失承担任何责任。您对[FunASR 软件]的使用和修改应该自行承担风险。
4 终止
如果您违反本协议的任何条款，您的许可将自动终止，您必须停止使用、复制、修改和分享[FunASR 软件]。
5 修订
本协议可能会不时更新和修订。修订后的协议将在[FunASR 软件]官方仓库发布，并自动生效。如果您继续使用、复制、修改和分享[FunASR 软件]，即表示您同意修订后的协议。
6 其他规定
本协议受到[国家/地区] 的法律管辖。如果任何条款被裁定为不合法、无效或无法执行，则该条款应被视为从本协议中删除，而其余条款应继续有效并具有约束力。
如果您对本协议有任何问题或意见，请联系我们。
版权所有© [2023-2028] [阿里巴巴集团]。保留所有权利。
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[//]: # (<div align="left"><img src="docs/images/funasr_logo.jpg" width="400"/></div>)
([简体中文](./README_zh.md)|English)
[//]: # (# FunASR: A Fundamental End-to-End Speech Recognition Toolkit)
[![SVG Banners](https://svg-banners.vercel.app/api?type=origin&text1=FunASR🤠&text2=💖%20A%20Fundamental%20End-to-End%20Speech%20Recognition%20Toolkit&width=800&height=210)](https://github.com/Akshay090/svg-banners)
[![PyPI](https://img.shields.io/pypi/v/funasr)](https://pypi.org/project/funasr/)
<strong>FunASR</strong> hopes to build a bridge between academic research and industrial applications on speech recognition. By supporting the training & finetuning of the industrial-grade speech recognition model, researchers and developers can conduct research and production of speech recognition models more conveniently, and promote the development of speech recognition ecology. ASR for Fun！
[**Highlights**](#highlights)
| [**News**](https://github.com/alibaba-damo-academy/FunASR#whats-new)
| [**Installation**](#installation)
| [**Quick Start**](#quick-start)
| [**Tutorial**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/tutorial/README.md)
| [**Runtime**](./runtime/readme.md)
| [**Model Zoo**](#model-zoo)
| [**Contact**](#contact)
<a name="highlights"></a>
## Highlights
- FunASR is a fundamental speech recognition toolkit that offers a variety of features, including speech recognition (ASR), Voice Activity Detection (VAD), Punctuation Restoration, Language Models, Speaker Verification, Speaker Diarization and multi-talker ASR. FunASR provides convenient scripts and tutorials, supporting inference and fine-tuning of pre-trained models.
- We have released a vast collection of academic and industrial pretrained models on the [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition) and [huggingface](https://huggingface.co/FunASR), which can be accessed through our [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md). The representative [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary), a non-autoregressive end-to-end speech recognition model, has the advantages of high accuracy, high efficiency, and convenient deployment, supporting the rapid construction of speech recognition services. For more details on service deployment, please refer to the [service deployment document](runtime/readme_cn.md).
<a name="whats-new"></a>
## What's new:
- 2024/09/26: Offline File Transcription Service 4.6, Offline File Transcription Service of English 1.7，Real-time Transcription Service 1.11 released，fix memory leak & Support the SensevoiceSmall onnx model；File Transcription Service 2.0 GPU released, Fix GPU memory leak; ([docs](runtime/readme.md));
- 2024/09/25：keyword spotting models are new supported. Supports fine-tuning and inference for four models: [fsmn_kws](https://modelscope.cn/models/iic/speech_sanm_kws_phone-xiaoyun-commands-online), [fsmn_kws_mt](https://modelscope.cn/models/iic/speech_sanm_kws_phone-xiaoyun-commands-online), [sanm_kws](https://modelscope.cn/models/iic/speech_sanm_kws_phone-xiaoyun-commands-offline), [sanm_kws_streaming](https://modelscope.cn/models/iic/speech_sanm_kws_phone-xiaoyun-commands-online).
- 2024/07/04：[SenseVoice](https://github.com/FunAudioLLM/SenseVoice) is a speech foundation model with multiple speech understanding capabilities, including ASR, LID, SER, and AED.
- 2024/07/01: Offline File Transcription Service GPU 1.1 released, optimize BladeDISC model compatibility issues; ref to ([docs](runtime/readme.md))
- 2024/06/27: Offline File Transcription Service GPU 1.0 released, supporting dynamic batch processing and multi-threading concurrency. In the long audio test set, the single-thread RTF is 0.0076, and multi-threads' speedup is 1200+ (compared to 330+ on CPU); ref to ([docs](runtime/readme.md))
- 2024/05/15：emotion recognition models are new supported. [emotion2vec+large](https://modelscope.cn/models/iic/emotion2vec_plus_large/summary)，[emotion2vec+base](https://modelscope.cn/models/iic/emotion2vec_plus_base/summary)，[emotion2vec+seed](https://modelscope.cn/models/iic/emotion2vec_plus_seed/summary). currently supports the following categories: 0: angry 1: happy 2: neutral 3: sad 4: unknown.
- 2024/05/15: Offline File Transcription Service 4.5, Offline File Transcription Service of English 1.6，Real-time Transcription Service 1.10 released，adapting to FunASR 1.0 model structure；([docs](runtime/readme.md))
- 2024/03/05：Added the Qwen-Audio and Qwen-Audio-Chat large-scale audio-text multimodal models, which have topped multiple audio domain leaderboards. These models support speech dialogue, [usage](examples/industrial_data_pretraining/qwen_audio).
- 2024/03/05：Added support for the Whisper-large-v3 model, a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. It can be downloaded from the[modelscope](examples/industrial_data_pretraining/whisper/demo.py), and [openai](examples/industrial_data_pretraining/whisper/demo_from_openai.py).
- 2024/03/05: Offline File Transcription Service 4.4, Offline File Transcription Service of English 1.5，Real-time Transcription Service 1.9 released，docker image supports ARM64 platform, update modelscope；([docs](runtime/readme.md))
- 2024/01/30：funasr-1.0 has been released ([docs](https://github.com/alibaba-damo-academy/FunASR/discussions/1319))
<details><summary>Full Changelog</summary>
- 2024/01/30：emotion recognition models are new supported. [model link](https://www.modelscope.cn/models/iic/emotion2vec_base_finetuned/summary), modified from [repo](https://github.com/ddlBoJack/emotion2vec).
- 2024/01/25: Offline File Transcription Service 4.2, Offline File Transcription Service of English 1.3 released，optimized the VAD (Voice Activity Detection) data processing method, significantly reducing peak memory usage, memory leak optimization; Real-time Transcription Service 1.7 released，optimizatized the client-side；([docs](runtime/readme.md))
- 2024/01/09: The Funasr SDK for Windows version 2.0 has been released, featuring support for The offline file transcription service (CPU) of Mandarin 4.1, The offline file transcription service (CPU) of English 1.2, The real-time transcription service (CPU) of Mandarin 1.6. For more details, please refer to the official documentation or release notes([FunASR-Runtime-Windows](https://www.modelscope.cn/models/damo/funasr-runtime-win-cpu-x64/summary))
- 2024/01/03: File Transcription Service 4.0 released, Added support for 8k models, optimized timestamp mismatch issues and added sentence-level timestamps, improved the effectiveness of English word FST hotwords, supported automated configuration of thread parameters, and fixed known crash issues as well as memory leak problems, refer to ([docs](runtime/readme.md#file-transcription-service-mandarin-cpu)).
- 2024/01/03: Real-time Transcription Service 1.6 released，The 2pass-offline mode supports Ngram language model decoding and WFST hotwords, while also addressing known crash issues and memory leak problems, ([docs](runtime/readme.md#the-real-time-transcription-service-mandarin-cpu))
- 2024/01/03: Fixed known crash issues as well as memory leak problems, ([docs](runtime/readme.md#file-transcription-service-english-cpu)).
- 2023/12/04: The Funasr SDK for Windows version 1.0 has been released, featuring support for The offline file transcription service (CPU) of Mandarin, The offline file transcription service (CPU) of English, The real-time transcription service (CPU) of Mandarin. For more details, please refer to the official documentation or release notes([FunASR-Runtime-Windows](https://www.modelscope.cn/models/damo/funasr-runtime-win-cpu-x64/summary))
- 2023/11/08: The offline file transcription service 3.0 (CPU) of Mandarin has been released, adding punctuation large model, Ngram language model, and wfst hot words. For detailed information, please refer to [docs](runtime#file-transcription-service-mandarin-cpu).
- 2023/10/17: The offline file transcription service (CPU) of English has been released. For more details, please refer to ([docs](runtime#file-transcription-service-english-cpu)).
- 2023/10/13: [SlideSpeech](https://slidespeech.github.io/): A large scale multi-modal audio-visual corpus with a significant amount of real-time synchronized slides.
- 2023/10/10: The ASR-SpeakersDiarization combined pipeline [Paraformer-VAD-SPK](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr_vad_spk/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/demo.py) is now released. Experience the model to get recognition results with speaker information.
- 2023/10/07: [FunCodec](https://github.com/alibaba-damo-academy/FunCodec): A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec.
- 2023/09/01: The offline file transcription service 2.0 (CPU) of Mandarin has been released, with added support for ffmpeg, timestamp, and hotword models. For more details, please refer to ([docs](runtime#file-transcription-service-mandarin-cpu)).
- 2023/08/07: The real-time transcription service (CPU) of Mandarin has been released. For more details, please refer to ([docs](runtime#the-real-time-transcription-service-mandarin-cpu)).
- 2023/07/17: BAT is released, which is a low-latency and low-memory-consumption RNN-T model. For more details, please refer to ([BAT](egs/aishell/bat)).
- 2023/06/26: ASRU2023 Multi-Channel Multi-Party Meeting Transcription Challenge 2.0 completed the competition and announced the results. For more details, please refer to ([M2MeT2.0](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)).
</details>
<a name="Installation"></a>
## Installation
- Requirements
```text
python>=3.8
torch>=1.13
torchaudio
```
- Install for pypi
```shell
pip3 install -U funasr
```
- Or install from source code
``` sh
git clone https://github.com/alibaba/FunASR.git && cd FunASR
pip3 install -e ./
```
- Install modelscope or huggingface_hub for the pretrained models (Optional)
```shell
pip3 install -U modelscope huggingface_hub
```
## Model Zoo
FunASR has open-sourced a large number of pre-trained models on industrial data. You are free to use, copy, modify, and share FunASR models under the [Model License Agreement](./MODEL_LICENSE). Below are some representative models, for more models please refer to the [Model Zoo](./model_zoo).
(Note: ⭐ represents the ModelScope model zoo, 🤗 represents the Huggingface model zoo, 🍀 represents the OpenAI model zoo)
| Model Name | Task Details | Training Data | Parameters |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------:|:--------------------------------:|:----------:|
| SenseVoiceSmall <br> ([⭐](https://www.modelscope.cn/models/iic/SenseVoiceSmall) [🤗](https://huggingface.co/FunAudioLLM/SenseVoiceSmall) ) | multiple speech understanding capabilities, including ASR, ITN, LID, SER, and AED, support languages such as zh, yue, en, ja, ko | 300000 hours | 234M |
| paraformer-zh <br> ([⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) [🤗](https://huggingface.co/funasr/paraformer-zh) ) | speech recognition, with timestamps, non-streaming | 60000 hours, Mandarin | 220M |
| <nobr>paraformer-zh-streaming <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary) [🤗](https://huggingface.co/funasr/paraformer-zh-streaming) )</nobr> | speech recognition, streaming | 60000 hours, Mandarin | 220M |
| paraformer-en <br> ( [⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/summary) [🤗](https://huggingface.co/funasr/paraformer-en) ) | speech recognition, without timestamps, non-streaming | 50000 hours, English | 220M |
| conformer-en <br> ( [⭐](https://modelscope.cn/models/damo/speech_conformer_asr-en-16k-vocab4199-pytorch/summary) [🤗](https://huggingface.co/funasr/conformer-en) ) | speech recognition, non-streaming | 50000 hours, English | 220M |
| ct-punc <br> ( [⭐](https://modelscope.cn/models/damo/punc_ct-transformer_cn-en-common-vocab471067-large/summary) [🤗](https://huggingface.co/funasr/ct-punc) ) | punctuation restoration | 100M, Mandarin and English | 290M |
| fsmn-vad <br> ( [⭐](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) [🤗](https://huggingface.co/funasr/fsmn-vad) ) | voice activity detection | 5000 hours, Mandarin and English | 0.4M |
| fsmn-kws <br> ( [⭐](https://modelscope.cn/models/iic/speech_charctc_kws_phone-xiaoyun/summary) ) | keyword spotting，streaming | 5000 hours, Mandarin | 0.7M |
| fa-zh <br> ( [⭐](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) [🤗](https://huggingface.co/funasr/fa-zh) ) | timestamp prediction | 5000 hours, Mandarin | 38M |
| cam++ <br> ( [⭐](https://modelscope.cn/models/iic/speech_campplus_sv_zh-cn_16k-common/summary) [🤗](https://huggingface.co/funasr/campplus) ) | speaker verification/diarization | 5000 hours | 7.2M |
| Whisper-large-v2 <br> ([⭐](https://www.modelscope.cn/models/iic/speech_whisper-large_asr_multilingual/summary) [🍀](https://github.com/openai/whisper) ) | speech recognition, with timestamps, non-streaming | multilingual | 1550 M |
| Whisper-large-v3 <br> ([⭐](https://www.modelscope.cn/models/iic/Whisper-large-v3/summary) [🍀](https://github.com/openai/whisper) ) | speech recognition, with timestamps, non-streaming | multilingual | 1550 M |
| Qwen-Audio <br> ([⭐](examples/industrial_data_pretraining/qwen_audio/demo.py) [🤗](https://huggingface.co/Qwen/Qwen-Audio) ) | audio-text multimodal models (pretraining) | multilingual | 8B |
| Qwen-Audio-Chat <br> ([⭐](examples/industrial_data_pretraining/qwen_audio/demo_chat.py) [🤗](https://huggingface.co/Qwen/Qwen-Audio-Chat) ) | audio-text multimodal models (chat) | multilingual | 8B |
| emotion2vec+large <br> ([⭐](https://modelscope.cn/models/iic/emotion2vec_plus_large/summary) [🤗](https://huggingface.co/emotion2vec/emotion2vec_plus_large) ) | speech emotion recongintion | 40000 hours | 300M |
[//]: # ()
[//]: # (FunASR supports pre-trained or further fine-tuned models for deployment as a service. The CPU version of the Chinese offline file conversion service has been released, details can be found in [docs]&#40;funasr/runtime/docs/SDK_tutorial.md&#41;. More detailed information about service deployment can be found in the [deployment roadmap]&#40;funasr/runtime/readme_cn.md&#41;.)
<a name="quick-start"></a>
## Quick Start
Below is a quick start tutorial. Test audio files ([Mandarin](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav), [English](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_en.wav)).
### Command-line usage
```shell
funasr ++model=paraformer-zh ++vad_model="fsmn-vad" ++punc_model="ct-punc" ++input=asr_example_zh.wav
```
Notes: Support recognition of single audio file, as well as file list in Kaldi-style wav.scp format: `wav_id wav_pat`
### Speech Recognition (Non-streaming)
#### SenseVoice
```python
from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess
model_dir = "iic/SenseVoiceSmall"
model = AutoModel(
model=model_dir,
vad_model="fsmn-vad",
vad_kwargs={"max_single_segment_time": 30000},
device="cuda:0",
)
# en
res = model.generate(
input=f"{model.model_path}/example/en.mp3",
cache={},
language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
use_itn=True,
batch_size_s=60,
merge_vad=True, #
merge_length_s=15,
)
text = rich_transcription_postprocess(res[0]["text"])
print(text)
```
Parameter Description:
- `model_dir`: The name of the model, or the path to the model on the local disk.
- `vad_model`: This indicates the activation of VAD (Voice Activity Detection). The purpose of VAD is to split long audio into shorter clips. In this case, the inference time includes both VAD and SenseVoice total consumption, and represents the end-to-end latency. If you wish to test the SenseVoice model's inference time separately, the VAD model can be disabled.
- `vad_kwargs`: Specifies the configurations for the VAD model. `max_single_segment_time`: denotes the maximum duration for audio segmentation by the `vad_model`, with the unit being milliseconds (ms).
- `use_itn`: Whether the output result includes punctuation and inverse text normalization.
- `batch_size_s`: Indicates the use of dynamic batching, where the total duration of audio in the batch is measured in seconds (s).
- `merge_vad`: Whether to merge short audio fragments segmented by the VAD model, with the merged length being `merge_length_s`, in seconds (s).
- `ban_emo_unk`: Whether to ban the output of the `emo_unk` token.
#### Paraformer
```python
from funasr import AutoModel
# paraformer-zh is a multi-functional asr model
# use vad, punc, spk or not as you need
model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc",
# spk_model="cam++",
)
res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
batch_size_s=300,
hotword='魔搭')
print(res)
```
Note: `hub`: represents the model repository, `ms` stands for selecting ModelScope download, `hf` stands for selecting Huggingface download.
### Speech Recognition (Streaming)
```python
from funasr import AutoModel
chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention
model = AutoModel(model="paraformer-zh-streaming")
import soundfile
import os
wav_file = os.path.join(model.model_path, "example/asr_example.wav")
speech, sample_rate = soundfile.read(wav_file)
chunk_stride = chunk_size[1] * 960 # 600ms
cache = {}
total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
print(res)
```
Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `10*60=600ms`, and the lookahead information is `5*60=300ms`. Each inference input is `600ms` (sample points are `16000*0.6=960`), and the output is the corresponding text. For the last speech segment input, `is_final=True` needs to be set to force the output of the last word.
<details><summary>More Examples</summary>
### Voice Activity Detection (Non-Streaming)
```python
from funasr import AutoModel
model = AutoModel(model="fsmn-vad")
wav_file = f"{model.model_path}/example/vad_example.wav"
res = model.generate(input=wav_file)
print(res)
```
Note: The output format of the VAD model is: `[[beg1, end1], [beg2, end2], ..., [begN, endN]]`, where `begN/endN` indicates the starting/ending point of the `N-th` valid audio segment, measured in milliseconds.
### Voice Activity Detection (Streaming)
```python
from funasr import AutoModel
chunk_size = 200 # ms
model = AutoModel(model="fsmn-vad")
import soundfile
wav_file = f"{model.model_path}/example/vad_example.wav"
speech, sample_rate = soundfile.read(wav_file)
chunk_stride = int(chunk_size * sample_rate / 1000)
cache = {}
total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
if len(res[0]["value"]):
print(res)
```
Note: The output format for the streaming VAD model can be one of four scenarios:
- `[[beg1, end1], [beg2, end2], .., [begN, endN]]`：The same as the offline VAD output result mentioned above.
- `[[beg, -1]]`：Indicates that only a starting point has been detected.
- `[[-1, end]]`：Indicates that only an ending point has been detected.
- `[]`：Indicates that neither a starting point nor an ending point has been detected.
The output is measured in milliseconds and represents the absolute time from the starting point.
### Punctuation Restoration
```python
from funasr import AutoModel
model = AutoModel(model="ct-punc")
res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
print(res)
```
### Timestamp Prediction
```python
from funasr import AutoModel
model = AutoModel(model="fa-zh")
wav_file = f"{model.model_path}/example/asr_example.wav"
text_file = f"{model.model_path}/example/text.txt"
res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
print(res)
```
### Speech Emotion Recognition
```python
from funasr import AutoModel
model = AutoModel(model="emotion2vec_plus_large")
wav_file = f"{model.model_path}/example/test.wav"
res = model.generate(wav_file, output_dir="./outputs", granularity="utterance", extract_embedding=False)
print(res)
```
More usages ref to [docs](docs/tutorial/README_zh.md),
more examples ref to [demo](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)
</details>
## Export ONNX
### Command-line usage
```shell
funasr-export ++model=paraformer ++quantize=false ++device=cpu
```
### Python
```python
from funasr import AutoModel
model = AutoModel(model="paraformer", device="cpu")
res = model.export(quantize=False)
```
### Test ONNX
```python
# pip3 install -U funasr-onnx
from funasr_onnx import Paraformer
model_dir = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model = Paraformer(model_dir, batch_size=1, quantize=True)
wav_path = ['~/.cache/modelscope/hub/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']
result = model(wav_path)
print(result)
```
More examples ref to [demo](runtime/python/onnxruntime)
## Deployment Service
FunASR supports deploying pre-trained or further fine-tuned models for service. Currently, it supports the following types of service deployment:
- File transcription service, Mandarin, CPU version, done
- The real-time transcription service, Mandarin (CPU), done
- File transcription service, English, CPU version, done
- File transcription service, Mandarin, GPU version, in progress
- and more.
For more detailed information, please refer to the [service deployment documentation](runtime/readme.md).
<a name="contact"></a>
## Community Communication
If you encounter problems in use, you can directly raise Issues on the github page.
You can also scan the following DingTalk group to join the community group for communication and discussion.
| DingTalk group |
|:-------------------------------------------------------------------:|
| <div align="left"><img src="docs/images/dingding.png" width="250"/> |
## Contributors
| <div align="left"><img src="docs/images/alibaba.png" width="260"/> | <div align="left"><img src="docs/images/nwpu.png" width="260"/> | <img src="docs/images/China_Telecom.png" width="200"/> </div> | <img src="docs/images/RapidAI.png" width="200"/> </div> | <img src="docs/images/aihealthx.png" width="200"/> </div> | <img src="docs/images/XVERSE.png" width="250"/> </div> |
|:------------------------------------------------------------------:|:---------------------------------------------------------------:|:--------------------------------------------------------------:|:-------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------:|
The contributors can be found in [contributors list](./Acknowledge.md)
## License
This project is licensed under [The MIT License](https://opensource.org/licenses/MIT). FunASR also contains various third-party components and some code modified from other repos under other open source licenses.
The use of pretraining model is subject to [model license](./MODEL_LICENSE)
## Citations
``` bibtex
@inproceedings{gao2023funasr,
author={Zhifu Gao and Zerui Li and Jiaming Wang and Haoneng Luo and Xian Shi and Mengzhe Chen and Yabin Li and Lingyun Zuo and Zhihao Du and Zhangyu Xiao and Shiliang Zhang},
title={FunASR: A Fundamental End-to-End Speech Recognition Toolkit},
year={2023},
booktitle={INTERSPEECH},
}
@inproceedings{An2023bat,
author={Keyu An and Xian Shi and Shiliang Zhang},
title={BAT: Boundary aware transducer for memory-efficient and low-latency ASR},
year={2023},
booktitle={INTERSPEECH},
}
@inproceedings{gao22b_interspeech,
author={Zhifu Gao and ShiLiang Zhang and Ian McLoughlin and Zhijie Yan},
title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
year=2022,
booktitle={Proc. Interspeech 2022},
pages={2063--2067},
doi={10.21437/Interspeech.2022-9996}
}
@inproceedings{shi2023seaco,
author={Xian Shi and Yexin Yang and Zerui Li and Yanni Chen and Zhifu Gao and Shiliang Zhang},
title={SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability},
year={2023},
booktitle={ICASSP2024}
}
```
FunASR/README_zh.md 0 → 100644
View file @ 70a8a9e0
[//]: # (<div align="left"><img src="docs/images/funasr_logo.jpg" width="400"/></div>)
(简体中文|[English](./README.md))
[![SVG Banners](https://svg-banners.vercel.app/api?type=origin&text1=FunASR🤠&text2=💖%20A%20Fundamental%20End-to-End%20Speech%20Recognition%20Toolkit&width=800&height=210)](https://github.com/Akshay090/svg-banners)
[//]: # (# FunASR: A Fundamental End-to-End Speech Recognition Toolkit)
[![PyPI](https://img.shields.io/pypi/v/funasr)](https://pypi.org/project/funasr/)
FunASR希望在语音识别的学术研究和工业应用之间架起一座桥梁。通过发布工业级语音识别模型的训练和微调，研究人员和开发人员可以更方便地进行语音识别模型的研究和生产，并推动语音识别生态的发展。让语音识别更有趣！
<div align="center">
<h4>
<a href="#核心功能"> 核心功能 </a>
｜<a href="#最新动态"> 最新动态 </a>
｜<a href="#安装教程"> 安装 </a>
｜<a href="#快速开始"> 快速开始 </a>
｜<a href="https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/tutorial/README_zh.md"> 教程文档 </a>
｜<a href="#模型仓库"> 模型仓库 </a>
｜<a href="#服务部署"> 服务部署 </a>
｜<a href="#联系我们"> 联系我们 </a>
</h4>
</div>
<a name="核心功能"></a>
## 核心功能
- FunASR是一个基础语音识别工具包，提供多种功能，包括语音识别（ASR）、语音端点检测（VAD）、标点恢复、语言模型、说话人验证、说话人分离和多人对话语音识别等。FunASR提供了便捷的脚本和教程，支持预训练好的模型的推理与微调。
- 我们在[ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition)与[huggingface](https://huggingface.co/FunASR)上发布了大量开源数据集或者海量工业数据训练的模型，可以通过我们的[模型仓库](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md)了解模型的详细信息。代表性的[Paraformer](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)非自回归端到端语音识别模型具有高精度、高效率、便捷部署的优点，支持快速构建语音识别服务，详细信息可以阅读([服务部署文档](runtime/readme_cn.md))。
<a name="最新动态"></a>
## 最新动态
- 2024/09/26: 中文离线文件转写服务 4.6、英文离线文件转写服务 1.7、中文实时语音听写服务 1.11 发布，修复ONNX内存泄漏、支持SensevoiceSmall onnx模型；中文离线文件转写服务GPU 2.0 发布，修复显存泄漏; 详细信息参阅([部署文档](runtime/readme_cn.md))
- 2024/09/25：新增语音唤醒模型，支持[fsmn_kws](https://modelscope.cn/models/iic/speech_sanm_kws_phone-xiaoyun-commands-online), [fsmn_kws_mt](https://modelscope.cn/models/iic/speech_sanm_kws_phone-xiaoyun-commands-online), [sanm_kws](https://modelscope.cn/models/iic/speech_sanm_kws_phone-xiaoyun-commands-offline), [sanm_kws_streaming](https://modelscope.cn/models/iic/speech_sanm_kws_phone-xiaoyun-commands-online) 4个模型的微调和推理。
- 2024/07/04：[SenseVoice](https://github.com/FunAudioLLM/SenseVoice) 是一个基础语音理解模型，具备多种语音理解能力，涵盖了自动语音识别（ASR）、语言识别（LID）、情感识别（SER）以及音频事件检测（AED）。
- 2024/07/01：中文离线文件转写服务GPU版本 1.1发布，优化bladedisc模型兼容性问题；详细信息参阅([部署文档](runtime/readme_cn.md))
- 2024/06/27：中文离线文件转写服务GPU版本 1.0发布，支持动态batch，支持多路并发，在长音频测试集上单线RTF为0.0076，多线加速比为1200+（CPU为330+）；详细信息参阅([部署文档](runtime/readme_cn.md))
- 2024/05/15：新增加情感识别模型，[emotion2vec+large](https://modelscope.cn/models/iic/emotion2vec_plus_large/summary)，[emotion2vec+base](https://modelscope.cn/models/iic/emotion2vec_plus_base/summary)，[emotion2vec+seed](https://modelscope.cn/models/iic/emotion2vec_plus_seed/summary)，输出情感类别为：生气/angry，开心/happy，中立/neutral，难过/sad。
- 2024/05/15: 中文离线文件转写服务 4.5、英文离线文件转写服务 1.6、中文实时语音听写服务 1.10 发布，适配FunASR 1.0模型结构；详细信息参阅([部署文档](runtime/readme_cn.md))
- 2024/03/05：新增加Qwen-Audio与Qwen-Audio-Chat音频文本模态大模型，在多个音频领域测试榜单刷榜，中支持语音对话，详细用法见 [示例](examples/industrial_data_pretraining/qwen_audio)。
- 2024/03/05：新增加Whisper-large-v3模型支持，多语言语音识别/翻译/语种识别，支持从 [modelscope](examples/industrial_data_pretraining/whisper/demo.py)仓库下载，也支持从 [openai](examples/industrial_data_pretraining/whisper/demo_from_openai.py)仓库下载模型。
- 2024/03/05: 中文离线文件转写服务 4.4、英文离线文件转写服务 1.5、中文实时语音听写服务 1.9 发布，docker镜像支持arm64平台，升级modelscope版本；详细信息参阅([部署文档](runtime/readme_cn.md))
- 2024/01/30：funasr-1.0发布，更新说明[文档](https://github.com/alibaba-damo-academy/FunASR/discussions/1319)
<details><summary>展开日志</summary>
- 2024/01/30：新增加情感识别 [模型链接](https://www.modelscope.cn/models/iic/emotion2vec_base_finetuned/summary)，原始模型 [repo](https://github.com/ddlBoJack/emotion2vec).
- 2024/01/25: 中文离线文件转写服务 4.2、英文离线文件转写服务 1.3，优化vad数据处理方式，大幅降低峰值内存占用，内存泄漏优化；中文实时语音听写服务 1.7 发布，客户端优化；详细信息参阅([部署文档](runtime/readme_cn.md))
- 2024/01/09: funasr社区软件包windows 2.0版本发布，支持软件包中文离线文件转写4.1、英文离线文件转写1.2、中文实时听写服务1.6的最新功能，详细信息参阅([FunASR社区软件包windows版本](https://www.modelscope.cn/models/damo/funasr-runtime-win-cpu-x64/summary))
- 2024/01/03: 中文离线文件转写服务 4.0 发布，新增支持8k模型、优化时间戳不匹配问题及增加句子级别时间戳、优化英文单词fst热词效果、支持自动化配置线程参数，同时修复已知的crash问题及内存泄漏问题，详细信息参阅([部署文档](runtime/readme_cn.md#中文离线文件转写服务cpu版本))
- 2024/01/03: 中文实时语音听写服务 1.6 发布，2pass-offline模式支持Ngram语言模型解码、wfst热词，同时修复已知的crash问题及内存泄漏问题，详细信息参阅([部署文档](runtime/readme_cn.md#中文实时语音听写服务cpu版本))
- 2024/01/03: 英文离线文件转写服务 1.2 发布，修复已知的crash问题及内存泄漏问题，详细信息参阅([部署文档](runtime/readme_cn.md#英文离线文件转写服务cpu版本))
- 2023/12/04: funasr社区软件包windows 1.0版本发布，支持中文离线文件转写、英文离线文件转写、中文实时听写服务，详细信息参阅([FunASR社区软件包windows版本](https://www.modelscope.cn/models/damo/funasr-runtime-win-cpu-x64/summary))
- 2023/11/08：中文离线文件转写服务3.0 CPU版本发布，新增标点大模型、Ngram语言模型与wfst热词，详细信息参阅([部署文档](runtime/readme_cn.md#中文离线文件转写服务cpu版本))
- 2023/10/17: 英文离线文件转写服务一键部署的CPU版本发布，详细信息参阅([部署文档](runtime/readme_cn.md#英文离线文件转写服务cpu版本))
- 2023/10/13: [SlideSpeech](https://slidespeech.github.io/): 一个大规模的多模态音视频语料库，主要是在线会议或者在线课程场景，包含了大量与发言人讲话实时同步的幻灯片。
- 2023.10.10: [Paraformer-long-Spk](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr_vad_spk/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/demo.py)模型发布，支持在长语音识别的基础上获取每句话的说话人标签。
- 2023.10.07: [FunCodec](https://github.com/alibaba-damo-academy/FunCodec): FunCodec提供开源模型和训练工具，可以用于音频离散编码，以及基于离散编码的语音识别、语音合成等任务。
- 2023.09.01: 中文离线文件转写服务2.0 CPU版本发布，新增ffmpeg、时间戳与热词模型支持，详细信息参阅([部署文档](runtime/readme_cn.md#中文离线文件转写服务cpu版本))
- 2023.08.07: 中文实时语音听写服务一键部署的CPU版本发布，详细信息参阅([部署文档](runtime/readme_cn.md#中文实时语音听写服务cpu版本))
- 2023.07.17: BAT一种低延迟低内存消耗的RNN-T模型发布，详细信息参阅（[BAT](egs/aishell/bat)）
- 2023.06.26: ASRU2023 多通道多方会议转录挑战赛2.0完成竞赛结果公布，详细信息参阅（[M2MeT2.0](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)）
</details>
<a name="安装教程"></a>
## 安装教程
- 安装funasr之前，确保已经安装了下面依赖环境:
```text
python>=3.8
torch>=1.13
torchaudio
```
- pip安装
```shell
pip3 install -U funasr
```
- 或者从源代码安装
``` sh
git clone https://github.com/alibaba/FunASR.git && cd FunASR
pip3 install -e ./
```
如果需要使用工业预训练模型，安装modelscope与huggingface_hub（可选）
```shell
pip3 install -U modelscope huggingface huggingface_hub
```
## 模型仓库
FunASR开源了大量在工业数据上预训练模型，您可以在[模型许可协议](./MODEL_LICENSE)下自由使用、复制、修改和分享FunASR模型，下面列举代表性的模型，更多模型请参考 [模型仓库](./model_zoo)。
（注：⭐ 表示ModelScope模型仓库，🤗 表示Huggingface模型仓库，🍀表示OpenAI模型仓库）
| 模型名字 | 任务详情 | 训练数据 | 参数量 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------:|:--------------:|:------:|
| SenseVoiceSmall <br> ([⭐](https://www.modelscope.cn/models/iic/SenseVoiceSmall) [🤗](https://huggingface.co/FunAudioLLM/SenseVoiceSmall) ) | 多种语音理解能力，涵盖了自动语音识别（ASR）、语言识别（LID）、情感识别（SER）以及音频事件检测（AED） | 400000小时，中文 | 330M |
| paraformer-zh <br> ([⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) [🤗](https://huggingface.co/funasr/paraformer-zh) ) | 语音识别，带时间戳输出，非实时 | 60000小时，中文 | 220M |
| paraformer-zh-streaming <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary) [🤗](https://huggingface.co/funasr/paraformer-zh-streaming) ) | 语音识别，实时 | 60000小时，中文 | 220M |
| paraformer-en <br> ( [⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/summary) [🤗](https://huggingface.co/funasr/paraformer-en) ) | 语音识别，非实时 | 50000小时，英文 | 220M |
| conformer-en <br> ( [⭐](https://modelscope.cn/models/damo/speech_conformer_asr-en-16k-vocab4199-pytorch/summary) [🤗](https://huggingface.co/funasr/conformer-en) ) | 语音识别，非实时 | 50000小时，英文 | 220M |
| ct-punc <br> ( [⭐](https://modelscope.cn/models/damo/punc_ct-transformer_cn-en-common-vocab471067-large/summary) [🤗](https://huggingface.co/funasr/ct-punc) ) | 标点恢复 | 100M，中文与英文 | 290M |
| fsmn-vad <br> ( [⭐](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) [🤗](https://huggingface.co/funasr/fsmn-vad) ) | 语音端点检测，实时 | 5000小时，中文与英文 | 0.4M |
| fsmn-kws <br> ( [⭐](https://modelscope.cn/models/iic/speech_charctc_kws_phone-xiaoyun/summary) ) | 语音唤醒，实时 | 5000小时，中文 | 0.7M |
| fa-zh <br> ( [⭐](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) [🤗](https://huggingface.co/funasr/fa-zh) ) | 字级别时间戳预测 | 50000小时，中文 | 38M |
| cam++ <br> ( [⭐](https://modelscope.cn/models/iic/speech_campplus_sv_zh-cn_16k-common/summary) [🤗](https://huggingface.co/funasr/campplus) ) | 说话人确认/分割 | 5000小时 | 7.2M |
| Whisper-large-v3 <br> ([⭐](https://www.modelscope.cn/models/iic/Whisper-large-v3/summary) [🍀](https://github.com/openai/whisper) ) | 语音识别，带时间戳输出，非实时 | 多语言 | 1550 M |
| Qwen-Audio <br> ([⭐](examples/industrial_data_pretraining/qwen_audio/demo.py) [🤗](https://huggingface.co/Qwen/Qwen-Audio) ) | 音频文本多模态大模型（预训练） | 多语言 | 8B |
| Qwen-Audio-Chat <br> ([⭐](examples/industrial_data_pretraining/qwen_audio/demo_chat.py) [🤗](https://huggingface.co/Qwen/Qwen-Audio-Chat) ) | 音频文本多模态大模型（chat版本） | 多语言 | 8B |
| emotion2vec+large <br> ([⭐](https://modelscope.cn/models/iic/emotion2vec_plus_large/summary) [🤗](https://huggingface.co/emotion2vec/emotion2vec_plus_large) ) | 情感识别模型 | 40000小时，4种情感类别 | 300M |
<a name="快速开始"></a>
## 快速开始
下面为快速上手教程，测试音频（[中文](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav)，[英文](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_en.wav)）
### 可执行命令行
```shell
funasr ++model=paraformer-zh ++vad_model="fsmn-vad" ++punc_model="ct-punc" ++input=asr_example_zh.wav
```
注：支持单条音频文件识别，也支持文件列表，列表为kaldi风格wav.scp：`wav_id wav_path`
### 非实时语音识别
#### SenseVoice
```python
from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess
model_dir = "iic/SenseVoiceSmall"
model = AutoModel(
model=model_dir,
vad_model="fsmn-vad",
vad_kwargs={"max_single_segment_time": 30000},
device="cuda:0",
)
# en
res = model.generate(
input=f"{model.model_path}/example/en.mp3",
cache={},
language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
use_itn=True,
batch_size_s=60,
merge_vad=True, #
merge_length_s=15,
)
text = rich_transcription_postprocess(res[0]["text"])
print(text)
```
参数说明：
- `model_dir`：模型名称，或本地磁盘中的模型路径。
- `vad_model`：表示开启VAD，VAD的作用是将长音频切割成短音频，此时推理耗时包括了VAD与SenseVoice总耗时，为链路耗时，如果需要单独测试SenseVoice模型耗时，可以关闭VAD模型。
- `vad_kwargs`：表示VAD模型配置,`max_single_segment_time`: 表示`vad_model`最大切割音频时长, 单位是毫秒ms。
- `use_itn`：输出结果中是否包含标点与逆文本正则化。
- `batch_size_s` 表示采用动态batch，batch中总音频时长，单位为秒s。
- `merge_vad`：是否将 vad 模型切割的短音频碎片合成，合并后长度为`merge_length_s`，单位为秒s。
- `ban_emo_unk`：禁用emo_unk标签，禁用后所有的句子都会被赋与情感标签。
#### Paraformer
```python
from funasr import AutoModel
# paraformer-zh is a multi-functional asr model
# use vad, punc, spk or not as you need
model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc",
# spk_model="cam++"
)
res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
batch_size_s=300,
hotword='魔搭')
print(res)
```
注：`hub`：表示模型仓库，`ms`为选择modelscope下载，`hf`为选择huggingface下载。
### 实时语音识别
```python
from funasr import AutoModel
chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention
model = AutoModel(model="paraformer-zh-streaming")
import soundfile
import os
wav_file = os.path.join(model.model_path, "example/asr_example.wav")
speech, sample_rate = soundfile.read(wav_file)
chunk_stride = chunk_size[1] * 960 # 600ms
cache = {}
total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
print(res)
```
注：`chunk_size`为流式延时配置，`[0,10,5]`表示上屏实时出字粒度为`10*60=600ms`，未来信息为`5*60=300ms`。每次推理输入为`600ms`（采样点数为`16000*0.6=960`），输出为对应文字，最后一个语音片段输入需要设置`is_final=True`来强制输出最后一个字。
<details><summary>更多例子</summary>
### 语音端点检测（非实时）
```python
from funasr import AutoModel
model = AutoModel(model="fsmn-vad")
wav_file = f"{model.model_path}/example/vad_example.wav"
res = model.generate(input=wav_file)
print(res)
```
注：VAD模型输出格式为：`[[beg1, end1], [beg2, end2], .., [begN, endN]]`，其中`begN/endN`表示第`N`个有效音频片段的起始点/结束点，
单位为毫秒。
### 语音端点检测（实时）
```python
from funasr import AutoModel
chunk_size = 200 # ms
model = AutoModel(model="fsmn-vad")
import soundfile
wav_file = f"{model.model_path}/example/vad_example.wav"
speech, sample_rate = soundfile.read(wav_file)
chunk_stride = int(chunk_size * sample_rate / 1000)
cache = {}
total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
if len(res[0]["value"]):
print(res)
```
注：流式VAD模型输出格式为4种情况：
- `[[beg1, end1], [beg2, end2], .., [begN, endN]]`：同上离线VAD输出结果。
- `[[beg, -1]]`：表示只检测到起始点。
- `[[-1, end]]`：表示只检测到结束点。
- `[]`：表示既没有检测到起始点，也没有检测到结束点
输出结果单位为毫秒，从起始点开始的绝对时间。
### 标点恢复
```python
from funasr import AutoModel
model = AutoModel(model="ct-punc")
res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
print(res)
```
### 时间戳预测
```python
from funasr import AutoModel
model = AutoModel(model="fa-zh")
wav_file = f"{model.model_path}/example/asr_example.wav"
text_file = f"{model.model_path}/example/text.txt"
res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
print(res)
```
### 情感识别
```python
from funasr import AutoModel
model = AutoModel(model="emotion2vec_plus_large")
wav_file = f"{model.model_path}/example/test.wav"
res = model.generate(wav_file, output_dir="./outputs", granularity="utterance", extract_embedding=False)
print(res)
```
更详细（[教程文档](docs/tutorial/README_zh.md)），
更多（[模型示例](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)）
</details>
## 导出ONNX
### 从命令行导出
```shell
funasr-export ++model=paraformer ++quantize=false
```
### 从Python导出
```python
from funasr import AutoModel
model = AutoModel(model="paraformer")
res = model.export(quantize=False)
```
### 测试ONNX
```python
# pip3 install -U funasr-onnx
from funasr_onnx import Paraformer
model_dir = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model = Paraformer(model_dir, batch_size=1, quantize=True)
wav_path = ['~/.cache/modelscope/hub/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']
result = model(wav_path)
print(result)
```
更多例子请参考 [样例](runtime/python/onnxruntime)
<a name="服务部署"></a>
## 服务部署
FunASR支持预训练或者进一步微调的模型进行服务部署。目前支持以下几种服务部署：
- 中文离线文件转写服务（CPU版本），已完成
- 中文流式语音识别服务（CPU版本），已完成
- 英文离线文件转写服务（CPU版本），已完成
- 中文离线文件转写服务（GPU版本），进行中
- 更多支持中
详细信息可以参阅([服务部署文档](runtime/readme_cn.md))。
<a name="社区交流"></a>
## 联系我们
如果您在使用中遇到问题，可以直接在github页面提Issues。欢迎语音兴趣爱好者扫描以下的钉钉群二维码加入社区群，进行交流和讨论。
| 钉钉群 |
|:-------------------------------------------------------------------:|
| <div align="left"><img src="docs/images/dingding.png" width="250"/> |
## 社区贡献者
| <div align="left"><img src="docs/images/alibaba.png" width="260"/> | <div align="left"><img src="docs/images/nwpu.png" width="260"/> | <img src="docs/images/China_Telecom.png" width="200"/> </div> | <img src="docs/images/RapidAI.png" width="200"/> </div> | <img src="docs/images/aihealthx.png" width="200"/> </div> | <img src="docs/images/XVERSE.png" width="250"/> </div> |
|:------------------------------------------------------------------:|:---------------------------------------------------------------:|:--------------------------------------------------------------:|:-------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------:|
贡献者名单请参考（[致谢名单](./Acknowledge.md)）
## 许可协议
项目遵循[The MIT License](https://opensource.org/licenses/MIT)开源协议，模型许可协议请参考（[模型协议](./MODEL_LICENSE)）
## 论文引用
``` bibtex
@inproceedings{gao2023funasr,
author={Zhifu Gao and Zerui Li and Jiaming Wang and Haoneng Luo and Xian Shi and Mengzhe Chen and Yabin Li and Lingyun Zuo and Zhihao Du and Zhangyu Xiao and Shiliang Zhang},
title={FunASR: A Fundamental End-to-End Speech Recognition Toolkit},
year={2023},
booktitle={INTERSPEECH},
}
@inproceedings{An2023bat,
author={Keyu An and Xian Shi and Shiliang Zhang},
title={BAT: Boundary aware transducer for memory-efficient and low-latency ASR},
year={2023},
booktitle={INTERSPEECH},
}
@inproceedings{gao22b_interspeech,
author={Zhifu Gao and ShiLiang Zhang and Ian McLoughlin and Zhijie Yan},
title={{Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition}},
year=2022,
booktitle={Proc. Interspeech 2022},
pages={2063--2067},
doi={10.21437/Interspeech.2022-9996}
}
@article{shi2023seaco,
author={Xian Shi and Yexin Yang and Zerui Li and Yanni Chen and Zhifu Gao and Shiliang Zhang},
title={{SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability}},
year=2023,
journal={arXiv preprint arXiv:2308.03266(accepted by ICASSP2024)},
}
```
FunASR/benchmarks/benchmark_pipeline_cer.md 0 → 100644
View file @ 70a8a9e0
# Leaderboard IO
## Configuration
### Data set:
[Aishell1](https://www.openslr.org/33/): dev, test
[Aishell2](https://www.aishelltech.com/aishell_2): dev_ios, test_ios, test_android, test_mic
[WenetSpeech](https://github.com/wenet-e2e/WenetSpeech): dev, test_meeting, test_net
### Tools
#### [Install Requirements](https://alibaba-damo-academy.github.io/FunASR/en/installation/installation.html#installation)
Install ModelScope and FunASR from pip
```shell
pip install -U modelscope funasr
# For the users in China, you could install with the command:
#pip install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple
```
Or install FunASR from source code
```shell
git clone https://github.com/alibaba/FunASR.git && cd FunASR
pip install -e ./
# For the users in China, you could install with the command:
# pip install -e ./ -i https://mirror.sjtu.edu.cn/pypi/web/simple
```
#### Recipe
##### [Test CER](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/asr_pipeline.html#inference-with-multi-thread-cpus-or-multi-gpus)
set the `model`, `data_dir` and `output_dir` in `infer.sh`.
```shell
cd egs_modelscope/asr/TEMPLATE
bash infer.sh
```
## Benchmark CER
### Chinese Dataset
<table border="1">
<tr align="center">
<td style="border: 1px solid">Model</td>
<td style="border: 1px solid">Offline/Online</td>
<td colspan="2" style="border: 1px solid">Aishell1</td>
<td colspan="4" style="border: 1px solid">Aishell2</td>
<td colspan="3" style="border: 1px solid">WenetSpeech</td>
</tr>
<tr align="center">
<td style="border: 1px solid"></td>
<td style="border: 1px solid"></td>
<td style="border: 1px solid">dev</td>
<td style="border: 1px solid">test</td>
<td style="border: 1px solid">dev_ios</td>
<td style="border: 1px solid">test_ios</td>
<td style="border: 1px solid">test_android</td>
<td style="border: 1px solid">test_mic</td>
<td style="border: 1px solid">dev</td>
<td style="border: 1px solid">test_meeting</td>
<td style="border: 1px solid">test_net</td>
</tr>
<tr align="center">
<td style="border: 1px solid"> <a href="https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary">Paraformer-large</a> </td>
<td style="border: 1px solid">Offline</td>
<td style="border: 1px solid">1.76</td>
<td style="border: 1px solid">1.94</td>
<td style="border: 1px solid">2.79</td>
<td style="border: 1px solid">2.84</td>
<td style="border: 1px solid">3.08</td>
<td style="border: 1px solid">3.03</td>
<td style="border: 1px solid">3.43</td>
<td style="border: 1px solid">7.01</td>
<td style="border: 1px solid">6.66</td>
</tr>
<tr align="center">
<td style="border: 1px solid"> <a href="https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary">Paraformer-large-long</a> </td>
<td style="border: 1px solid">Offline</td>
<td style="border: 1px solid">1.80</td>
<td style="border: 1px solid">2.10</td>
<td style="border: 1px solid">2.78</td>
<td style="border: 1px solid">2.87</td>
<td style="border: 1px solid">3.12</td>
<td style="border: 1px solid">3.11</td>
<td style="border: 1px solid">3.44</td>
<td style="border: 1px solid">13.28</td>
<td style="border: 1px solid">7.08</td>
</tr>
<tr align="center">
<td style="border: 1px solid"> <a href="https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary">Paraformer-large-contextual</a> </td>
<td style="border: 1px solid">Offline</td>
<td style="border: 1px solid">1.76</td>
<td style="border: 1px solid">2.02</td>
<td style="border: 1px solid">2.73</td>
<td style="border: 1px solid">2.85</td>
<td style="border: 1px solid">2.98</td>
<td style="border: 1px solid">2.95</td>
<td style="border: 1px solid">3.42</td>
<td style="border: 1px solid">7.16</td>
<td style="border: 1px solid">6.72</td>
</tr>
<tr align="center">
<td style="border: 1px solid"> <a href="https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary">Paraformer-large-online</a> </td>
<td style="border: 1px solid">Online</td>
<td style="border: 1px solid">2.37</td>
<td style="border: 1px solid">3.34</td>
<td style="border: 1px solid">4.04</td>
<td style="border: 1px solid">3.86</td>
<td style="border: 1px solid">4.38</td>
<td style="border: 1px solid">4.21</td>
<td style="border: 1px solid">4.55</td>
<td style="border: 1px solid">10.64</td>
<td style="border: 1px solid">7.78</td>
</tr>
<tr align="center">
<td style="border: 1px solid"> <a href="https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary">Paraformer</a> </td>
<td style="border: 1px solid">Offline</td>
<td style="border: 1px solid">3.24</td>
<td style="border: 1px solid">3.69</td>
<td style="border: 1px solid">4.58</td>
<td style="border: 1px solid">4.63</td>
<td style="border: 1px solid">4.83</td>
<td style="border: 1px solid">4.71</td>
<td style="border: 1px solid">4.19</td>
<td style="border: 1px solid">8.32</td>
<td style="border: 1px solid">9.19</td>
</tr>
<tr align="center">
<td style="border: 1px solid"> <a href="https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-online/summary">UniASR</a> </td>
<td style="border: 1px solid">Online</td>
<td style="border: 1px solid">3.34</td>
<td style="border: 1px solid">3.99</td>
<td style="border: 1px solid">4.62</td>
<td style="border: 1px solid">4.52</td>
<td style="border: 1px solid">4.77</td>
<td style="border: 1px solid">4.73</td>
<td style="border: 1px solid">4.51</td>
<td style="border: 1px solid">10.63</td>
<td style="border: 1px solid">9.70</td>
</tr>
<tr align="center">
<td style="border: 1px solid"> <a href="https://modelscope.cn/models/damo/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline/summary">UniASR-large</a> </td>
<td style="border: 1px solid">Offline</td>
<td style="border: 1px solid">2.93</td>
<td style="border: 1px solid">3.48</td>
<td style="border: 1px solid">3.95</td>
<td style="border: 1px solid">3.87</td>
<td style="border: 1px solid">4.11</td>
<td style="border: 1px solid">4.11</td>
<td style="border: 1px solid">4.16</td>
<td style="border: 1px solid">10.09</td>
<td style="border: 1px solid">8.69</td>
</tr>
<tr align="center">
<td style="border: 1px solid"> <a href="https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-aishell1-pytorch/summary">Paraformer-aishell</a> </td>
<td style="border: 1px solid">Offline</td>
<td style="border: 1px solid">4.88</td>
<td style="border: 1px solid">5.43</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
</tr>
<tr align="center">
<td style="border: 1px solid"> <a href="https://modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary">ParaformerBert-aishell</a> </td>
<td style="border: 1px solid">Offline</td>
<td style="border: 1px solid">6.14</td>
<td style="border: 1px solid">7.01</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
</tr>
<tr align="center">
<td style="border: 1px solid"> <a href="https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary">Paraformer-aishell2</a> </td>
<td style="border: 1px solid">Offline</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">5.82</td>
<td style="border: 1px solid">6.30</td>
<td style="border: 1px solid">6.60</td>
<td style="border: 1px solid">5.83</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
</tr>
<tr align="center">
<td style="border: 1px solid"> <a href="https://www.modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary">ParaformerBert-aishell2</a> </td>
<td style="border: 1px solid">Offline</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">4.95</td>
<td style="border: 1px solid">5.45</td>
<td style="border: 1px solid">5.59</td>
<td style="border: 1px solid">5.83</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
<td style="border: 1px solid">-</td>
</tr>
</table>
### English Dataset
FunASR/data/list/train.jsonl 0 → 100644
View file @ 70a8a9e0
{"key": "BAC009S0764W0121", "source": "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0764W0121.wav", "source_len": 90, "target": "甚至出现交易几乎停滞的情况", "target_len": 13}
{"key": "BAC009S0916W0489", "source": "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0916W0489.wav", "source_len": 90, "target": "湖北一公司以员工名义贷款数十员工负债千万", "target_len": 20}
{"key": "asr_example_cn_en", "source": "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_cn_en.wav", "source_len": 91, "target": "所有只要处理 data 不管你是做 machine learning 做 deep learning 做 data analytics 做 data science 也好 scientist 也好通通都要都做的基本功啊那 again 先先对有一些也许对", "target_len": 19}
{"key": "ID0012W0014", "source": "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_en.wav", "source_len": 88, "target": "he tried to think how it could be", "target_len": 8}
FunASR/data/list/train_emo.txt 0 → 100644
View file @ 70a8a9e0
BAC009S0764W0121 <|NEUTRAL|>
BAC009S0916W0489 <|NEUTRAL|>
asr_example_cn_en <|NEUTRAL|>
ID0012W0014 <|NEUTRAL|>
FunASR/data/list/train_event.txt 0 → 100644
View file @ 70a8a9e0
BAC009S0764W0121 <|Speech|>
BAC009S0916W0489 <|Speech|>
asr_example_cn_en <|Speech|>
ID0012W0014 <|Speech|>
FunASR/data/list/train_text.txt 0 → 100644
View file @ 70a8a9e0
BAC009S0764W0121 甚至出现交易几乎停滞的情况
BAC009S0916W0489 湖北一公司以员工名义贷款数十员工负债千万
asr_example_cn_en 所有只要处理 data 不管你是做 machine learning 做 deep learning 做 data analytics 做 data science 也好 scientist 也好通通都要都做的基本功啊那 again 先先对有一些也许对
ID0012W0014 he tried to think how it could be
\ No newline at end of file
FunASR/data/list/train_text_language.txt 0 → 100644
View file @ 70a8a9e0
BAC009S0764W0121 <|zh|>
BAC009S0916W0489 <|zh|>
asr_example_cn_en <|zh|>
ID0012W0014 <|en|>
FunASR/data/list/train_wav.scp 0 → 100644
View file @ 70a8a9e0
BAC009S0764W0121 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0764W0121.wav
BAC009S0916W0489 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0916W0489.wav
asr_example_cn_en https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_cn_en.wav
ID0012W0014 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_en.wav
\ No newline at end of file
FunASR/data/list/val.jsonl 0 → 100644
View file @ 70a8a9e0
{"key": "ID0012W0013", "source": "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", "source_len": 88, "target": "欢迎大家来体验达摩院推出的语音识别模型", "target_len": 19}
{"key": "ID0012W0014", "source": "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_en.wav", "source_len": 88, "target": "he tried to think how it could be", "target_len": 8}
FunASR/data/list/val_text.txt 0 → 100644
View file @ 70a8a9e0
ID0012W0013 欢迎大家来体验达摩院推出的语音识别模型
ID0012W0014 he tried to think how it could be
\ No newline at end of file
FunASR/data/list/val_wav.scp 0 → 100644
View file @ 70a8a9e0
ID0012W0013 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav
ID0012W0014 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_en.wav
\ No newline at end of file
FunASR/docs/Makefile 0 → 100644
View file @ 70a8a9e0
# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line, and also
# from the environment for the first two.
SPHINXOPTS =
SPHINXBUILD = sphinx-build
SPHINXPROJ = FunASR
SOURCEDIR = .
BUILDDIR = _build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
\ No newline at end of file
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