python/FunASR-XL.git

			@@ -2,8 +2,9 @@

			([简体中文](./README_zh.md)\|English)

			# FunASR: A Fundamental End-to-End Speech Recognition Toolkit
			[//]: # (# 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/)

			@@ -14,6 +15,7 @@
			\| [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)
			@@ -27,10 +29,15 @@

			<a name="whats-new"></a>
			## What's new:
			- 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))
			@@ -48,6 +55,7 @@
			- 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
			@@ -67,14 +75,14 @@
			```

			## 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]().
			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 \|
			\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:\|:-----------------------------------------------------:\|:--------------------------------:\|:----------:\|
			\| 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-tp) ) \| speech recognition, with timestamps, non-streaming \| 60000 hours, Mandarin \| 220M \|
			\| 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 \|
			@@ -82,10 +90,11 @@
			\| 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 \|
			\| 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 \| 1.5G \|
			\| 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 \| 1.5G \|
			\| 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 \|
			\| 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 \|



			@@ -97,7 +106,7 @@
			<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]()).
			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

			@@ -112,7 +121,7 @@
			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-c",
			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",
			@@ -149,12 +158,14 @@
			```
			Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `1060=600ms`, and the lookahead information is `560=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/asr_example.wav"
			wav_file = f"{model.model_path}/example/vad_example.wav"
			res = model.generate(input=wav_file)
			print(res)
			```
			@@ -208,24 +219,54 @@
			print(res)
			```

			More examples ref to [docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)

			### 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
			funasr-export ++model=paraformer ++quantize=false ++device=cpu
			```

			### python
			### Python
			```python
			from funasr import AutoModel

			model = AutoModel(model="paraformer")
			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:
			@@ -244,9 +285,9 @@

			You can also scan the following DingTalk group or WeChat group QR code to join the community group for communication and discussion.

			\|DingTalk group \| WeChat group \|
			\|:---:\|:-----------------------------------------------------:\|
			\|<div align="left"><img src="docs/images/dingding.jpg" width="250"/> \| <img src="docs/images/wechat.png" width="215"/></div> \|
			\| DingTalk group \| WeChat group \|
			\|:-------------------------------------------------------------------:\|:-----------------------------------------------------:\|
			\| <div align="left"><img src="docs/images/dingding.png" width="250"/> \| <img src="docs/images/wechat.png" width="215"/></div> \|

			## Contributors