python/FunASR-XL.git

			@@ -27,8 +27,9 @@

			<a name="whats-new"></a>
			## What's new:
			- 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](https://www.modelscope.cn/models/iic/Whisper-large-v3/summary), and [openai](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining/whisper).
			- 2024/03/03: 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；([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))
			- 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))
			@@ -83,6 +84,8 @@
			\| 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 \|



			@@ -207,7 +210,37 @@

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

			[//]: # (FunASR supports inference and fine-tuning of models trained on industrial datasets of tens of thousands of hours. For more details, please refer to ([modelscope_egs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)). It also supports training and fine-tuning of models on academic standard datasets. For more details, please refer to([egs](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html)). The models include speech recognition (ASR), speech activity detection (VAD), punctuation recovery, language model, speaker verification, speaker separation, and multi-party conversation speech recognition. For a detailed list of models, please refer to the [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md):)

			## 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)
			```

			### Text 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:
			@@ -226,9 +259,9 @@

			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