From 30aa982bf29ceefaf52c0013c12c19adc57dea0e Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 27 四月 2023 21:11:04 +0800
Subject: [PATCH] docs
---
/dev/null | 118 ---------------------------------------
egs_modelscope/speaker_diarization/TEMPLATE/README.md | 6 +-
egs_modelscope/speaker_verification/TEMPLATE/README.md | 8 +-
egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/demo.py | 1
egs_modelscope/tp/TEMPLATE/README.md | 6 +-
egs_modelscope/vad/TEMPLATE/README.md | 6 +-
egs_modelscope/asr/TEMPLATE/README.md | 6 +-
egs_modelscope/punctuation/TEMPLATE/README.md | 6 +-
8 files changed, 20 insertions(+), 137 deletions(-)
diff --git a/README.md b/README.md
deleted file mode 100644
index 665f425..0000000
--- a/README.md
+++ /dev/null
@@ -1,118 +0,0 @@
-[//]: # (<div align="left"><img src="docs/images/funasr_logo.jpg" width="400"/></div>)
-
-# FunASR: A Fundamental End-to-End Speech Recognition Toolkit
-<p align="left">
- <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-brightgreen.svg"></a>
- <a href=""><img src="https://img.shields.io/badge/Python->=3.7,<=3.10-aff.svg"></a>
- <a href=""><img src="https://img.shields.io/badge/Pytorch-%3E%3D1.11-blue"></a>
-</p>
-
-<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 released on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition), 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锛�
-
-[**News**](https://github.com/alibaba-damo-academy/FunASR#whats-new)
-| [**Highlights**](#highlights)
-| [**Installation**](#installation)
-| [**Docs**](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
-| [**Tutorial**](https://github.com/alibaba-damo-academy/FunASR/wiki#funasr%E7%94%A8%E6%88%B7%E6%89%8B%E5%86%8C)
-| [**Papers**](https://github.com/alibaba-damo-academy/FunASR#citations)
-| [**Runtime**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime)
-| [**Model Zoo**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/modelscope_models.md)
-| [**Contact**](#contact)
-| [**M2MET2.0 Challenge**](https://github.com/alibaba-damo-academy/FunASR#multi-channel-multi-party-meeting-transcription-20-m2met20-challenge)
-
-## What's new:
-### Multi-Channel Multi-Party Meeting Transcription 2.0 (M2MET2.0) Challenge
-We are pleased to announce that the M2MeT2.0 challenge will be held in the near future. The baseline system is conducted on FunASR and is provided as a receipe of AliMeeting corpus. For more details you can see the guidence of M2MET2.0 ([CN](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)/[EN](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)).
-### Release notes
-For the release notes, please ref to [news](https://github.com/alibaba-damo-academy/FunASR/releases)
-
-## Highlights
-- FunASR supports speech recognition(ASR), Multi-talker ASR, Voice Activity Detection(VAD), Punctuation Restoration, Language Models, Speaker Verification and Speaker diarization.
-- We have released large number of academic and industrial pretrained models on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition)
-- The pretrained model [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) obtains the best performance on many tasks in [SpeechIO leaderboard](https://github.com/SpeechColab/Leaderboard)
-- FunASR supplies a easy-to-use pipeline to finetune pretrained models from [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition)
-- Compared to [Espnet](https://github.com/espnet/espnet) framework, the training speed of large-scale datasets in FunASR is much faster owning to the optimized dataloader.
-
-## Installation
-
-Install from pip
-```shell
-pip install -U 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 from source code
-
-
-``` sh
-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
-
-```
-If you want to use the pretrained models in ModelScope, you should install the modelscope:
-
-```shell
-pip install -U modelscope
-# For the users in China, you could install with the command:
-# pip install -U modelscope -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html -i https://mirror.sjtu.edu.cn/pypi/web/simple
-```
-
-For more details, please ref to [installation](https://alibaba-damo-academy.github.io/FunASR/en/installation.html)
-
-[//]: # ()
-[//]: # (## Usage)
-
-[//]: # (For users who are new to FunASR and ModelScope, please refer to FunASR Docs([CN](https://alibaba-damo-academy.github.io/FunASR/cn/index.html) / [EN](https://alibaba-damo-academy.github.io/FunASR/en/index.html)))
-
-## Contact
-
-If you have any questions about FunASR, please contact us by
-
-- email: [funasr@list.alibaba-inc.com](funasr@list.alibaba-inc.com)
-
-|Dingding group | Wechat group |
-|:---:|:-----------------------------------------------------:|
-|<div align="left"><img src="docs/images/dingding.jpg" width="250"/> | <img src="docs/images/wechat.png" width="232"/></div> |
-
-## Contributors
-
-| <div align="left"><img src="docs/images/damo.png" width="180"/> | <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/DeepScience.png" width="200"/> </div> |
-|:---------------------------------------------------------------:|:---------------------------------------------------------------:|:--------------------------------------------------------------:|:-------------------------------------------------------:|:-----------------------------------------------------------:|
-
-## 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 [DeepScience](https://www.deepscience.cn) for contributing the grpc service.
-
-## License
-This project is licensed under the [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.
-
-## Citations
-
-``` bibtex
-@inproceedings{gao2022paraformer,
- title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
- author={Gao, Zhifu and Zhang, Shiliang and McLoughlin, Ian and Yan, Zhijie},
- booktitle={INTERSPEECH},
- year={2022}
-}
-@inproceedings{gao2020universal,
- title={Universal ASR: Unifying Streaming and Non-Streaming ASR Using a Single Encoder-Decoder Model},
- author={Gao, Zhifu and Zhang, Shiliang and Lei, Ming and McLoughlin, Ian},
- booktitle={arXiv preprint arXiv:2010.14099},
- year={2020}
-}
-@inproceedings{Shi2023AchievingTP,
- title={Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model},
- author={Xian Shi and Yanni Chen and Shiliang Zhang and Zhijie Yan},
- booktitle={arXiv preprint arXiv:2301.12343}
- year={2023}
-}
-```
diff --git a/egs_modelscope/asr/TEMPLATE/README.md b/egs_modelscope/asr/TEMPLATE/README.md
index 54af50f..28a31a2 100644
--- a/egs_modelscope/asr/TEMPLATE/README.md
+++ b/egs_modelscope/asr/TEMPLATE/README.md
@@ -76,15 +76,15 @@
print(rec_result)
```
-#### API-reference
-##### Define pipeline
+### API-reference
+#### Define pipeline
- `task`: `Tasks.auto_speech_recognition`
- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
- `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU
- `ncpu`: `1` (Default), sets the number of threads used for intraop parallelism on CPU
- `output_dir`: `None` (Default), the output path of results if set
- `batch_size`: `1` (Default), batch size when decoding
-##### Infer pipeline
+#### Infer pipeline
- `audio_in`: the input to decode, which could be:
- wav_path, `e.g.`: asr_example.wav,
- pcm_path, `e.g.`: asr_example.pcm,
diff --git a/egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/demo.py b/egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/demo.py
index c533e67..2fce734 100644
--- a/egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/demo.py
+++ b/egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/demo.py
@@ -9,6 +9,7 @@
model='damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
punc_model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
+ output_dir=output_dir
)
rec_result = inference_pipeline(audio_in=audio_in)
print(rec_result)
diff --git a/egs_modelscope/punctuation/TEMPLATE/README.md b/egs_modelscope/punctuation/TEMPLATE/README.md
index 19600d3..3eaf68a 100644
--- a/egs_modelscope/punctuation/TEMPLATE/README.md
+++ b/egs_modelscope/punctuation/TEMPLATE/README.md
@@ -52,15 +52,15 @@
Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/238)
-#### API-reference
-##### Define pipeline
+### API-reference
+#### Define pipeline
- `task`: `Tasks.punctuation`
- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
- `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU
- `output_dir`: `None` (Default), the output path of results if set
- `model_revision`: `None` (Default), setting the model version
-##### Infer pipeline
+#### Infer pipeline
- `text_in`: the input to decode, which could be:
- text bytes, `e.g.`: "鎴戜滑閮芥槸鏈ㄥご浜轰笉浼氳璇濅笉浼氬姩"
- text file, `e.g.`: example/punc_example.txt
diff --git a/egs_modelscope/speaker_diarization/TEMPLATE/README.md b/egs_modelscope/speaker_diarization/TEMPLATE/README.md
index 2cd702c..99c9b59 100644
--- a/egs_modelscope/speaker_diarization/TEMPLATE/README.md
+++ b/egs_modelscope/speaker_diarization/TEMPLATE/README.md
@@ -37,8 +37,8 @@
print(results)
```
-#### API-reference
-##### Define pipeline
+### API-reference
+#### Define pipeline
- `task`: `Tasks.speaker_diarization`
- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
- `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU
@@ -50,7 +50,7 @@
- vad format: spk1: [1.0, 3.0], [5.0, 8.0]
- rttm format: "SPEAKER test1 0 1.00 2.00 <NA> <NA> spk1 <NA> <NA>" and "SPEAKER test1 0 5.00 3.00 <NA> <NA> spk1 <NA> <NA>"
-##### Infer pipeline for speaker embedding extraction
+#### Infer pipeline for speaker embedding extraction
- `audio_in`: the input to process, which could be:
- list of url: `e.g.`: waveform files at a website
- list of local file path: `e.g.`: path/to/a.wav
diff --git a/egs_modelscope/speaker_verification/TEMPLATE/README.md b/egs_modelscope/speaker_verification/TEMPLATE/README.md
index 957da90..f7b64ce 100644
--- a/egs_modelscope/speaker_verification/TEMPLATE/README.md
+++ b/egs_modelscope/speaker_verification/TEMPLATE/README.md
@@ -47,8 +47,8 @@
```
Full code of demo, please ref to [infer.py](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/speaker_verification/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/infer.py).
-#### API-reference
-##### Define pipeline
+### API-reference
+#### Define pipeline
- `task`: `Tasks.speaker_verification`
- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
- `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU
@@ -57,7 +57,7 @@
- `sv_threshold`: `0.9465` (Default), the similarity threshold to determine
whether utterances belong to the same speaker (it should be in (0, 1))
-##### Infer pipeline for speaker embedding extraction
+#### Infer pipeline for speaker embedding extraction
- `audio_in`: the input to process, which could be:
- url (str): `e.g.`: https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav
- local_path: `e.g.`: path/to/a.wav
@@ -71,7 +71,7 @@
- fbank1.scp,speech,kaldi_ark: `e.g.`: extracted 80-dimensional fbank features
with kaldi toolkits.
-##### Infer pipeline for speaker verification
+#### Infer pipeline for speaker verification
- `audio_in`: the input to process, which could be:
- Tuple(url1, url2): `e.g.`: (https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav, https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_different.wav)
- Tuple(local_path1, local_path2): `e.g.`: (path/to/a.wav, path/to/b.wav)
diff --git a/egs_modelscope/tp/TEMPLATE/README.md b/egs_modelscope/tp/TEMPLATE/README.md
index 745249f..d33d4e6 100644
--- a/egs_modelscope/tp/TEMPLATE/README.md
+++ b/egs_modelscope/tp/TEMPLATE/README.md
@@ -23,15 +23,15 @@
-#### API-reference
-##### Define pipeline
+### API-reference
+#### Define pipeline
- `task`: `Tasks.speech_timestamp`
- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
- `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU
- `ncpu`: `1` (Default), sets the number of threads used for intraop parallelism on CPU
- `output_dir`: `None` (Default), the output path of results if set
- `batch_size`: `1` (Default), batch size when decoding
-##### Infer pipeline
+#### Infer pipeline
- `audio_in`: the input speech to predict, which could be:
- wav_path, `e.g.`: asr_example.wav (wav in local or url),
- wav.scp, kaldi style wav list (`wav_id wav_path`), `e.g.`:
diff --git a/egs_modelscope/vad/TEMPLATE/README.md b/egs_modelscope/vad/TEMPLATE/README.md
index 0542331..9ad9a1c 100644
--- a/egs_modelscope/vad/TEMPLATE/README.md
+++ b/egs_modelscope/vad/TEMPLATE/README.md
@@ -43,15 +43,15 @@
-#### API-reference
-##### Define pipeline
+### API-reference
+#### Define pipeline
- `task`: `Tasks.voice_activity_detection`
- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
- `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU
- `ncpu`: `1` (Default), sets the number of threads used for intraop parallelism on CPU
- `output_dir`: `None` (Default), the output path of results if set
- `batch_size`: `1` (Default), batch size when decoding
-##### Infer pipeline
+#### Infer pipeline
- `audio_in`: the input to decode, which could be:
- wav_path, `e.g.`: asr_example.wav,
- pcm_path, `e.g.`: asr_example.pcm,
--
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