| funasr/runtime/python/onnxruntime/funasr_onnx.egg-info/PKG-INFO | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| funasr/runtime/python/onnxruntime/funasr_onnx.egg-info/SOURCES.txt | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| funasr/runtime/python/onnxruntime/funasr_onnx.egg-info/dependency_links.txt | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| funasr/runtime/python/onnxruntime/funasr_onnx.egg-info/requires.txt | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| funasr/runtime/python/onnxruntime/funasr_onnx.egg-info/top_level.txt | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| funasr/runtime/python/websocket/ws_client.py | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| funasr/runtime/python/websocket/ws_server_2pass.py | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| funasr/runtime/python/websocket/ws_server_offline.py | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| funasr/runtime/python/websocket/ws_server_online.py | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| funasr/utils/modelscope_utils.py | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 |
funasr/runtime/python/onnxruntime/funasr_onnx.egg-info/PKG-INFO
New file @@ -0,0 +1,190 @@ Metadata-Version: 2.1 Name: funasr-onnx Version: 0.1.0 Summary: FunASR: A Fundamental End-to-End Speech Recognition Toolkit Home-page: https://github.com/alibaba-damo-academy/FunASR.git Author: Speech Lab of DAMO Academy, Alibaba Group Author-email: funasr@list.alibaba-inc.com License: MIT Keywords: funasr,asr Platform: Any Classifier: Programming Language :: Python :: 3.6 Classifier: Programming Language :: Python :: 3.7 Classifier: Programming Language :: Python :: 3.8 Classifier: Programming Language :: Python :: 3.9 Classifier: Programming Language :: Python :: 3.10 Description-Content-Type: text/markdown # ONNXRuntime-python ## Install `funasr_onnx` install from pip ```shell pip install -U funasr_onnx # For the users in China, you could install with the command: # pip install -U funasr_onnx -i https://mirror.sjtu.edu.cn/pypi/web/simple ``` or install from source code ```shell git clone https://github.com/alibaba/FunASR.git && cd FunASR cd funasr/runtime/python/onnxruntime 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 ``` ## Inference with runtime ### Speech Recognition #### Paraformer ```python from funasr_onnx import Paraformer from pathlib import Path 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'.format(Path.home())] result = model(wav_path) print(result) ``` - `model_dir`: model_name in modelscope or local path downloaded from modelscope. If the local path is set, it should contain `model.onnx`, `config.yaml`, `am.mvn` - `batch_size`: `1` (Default), the batch size duration inference - `device_id`: `-1` (Default), infer on CPU. If you want to infer with GPU, set it to gpu_id (Please make sure that you have install the onnxruntime-gpu) - `quantize`: `False` (Default), load the model of `model.onnx` in `model_dir`. If set `True`, load the model of `model_quant.onnx` in `model_dir` - `intra_op_num_threads`: `4` (Default), sets the number of threads used for intraop parallelism on CPU Input: wav formt file, support formats: `str, np.ndarray, List[str]` Output: `List[str]`: recognition result #### Paraformer-online ### Voice Activity Detection #### FSMN-VAD ```python from funasr_onnx import Fsmn_vad from pathlib import Path model_dir = "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch" wav_path = '{}/.cache/modelscope/hub/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/example/vad_example.wav'.format(Path.home()) model = Fsmn_vad(model_dir) result = model(wav_path) print(result) ``` - `model_dir`: model_name in modelscope or local path downloaded from modelscope. If the local path is set, it should contain `model.onnx`, `config.yaml`, `am.mvn` - `batch_size`: `1` (Default), the batch size duration inference - `device_id`: `-1` (Default), infer on CPU. If you want to infer with GPU, set it to gpu_id (Please make sure that you have install the onnxruntime-gpu) - `quantize`: `False` (Default), load the model of `model.onnx` in `model_dir`. If set `True`, load the model of `model_quant.onnx` in `model_dir` - `intra_op_num_threads`: `4` (Default), sets the number of threads used for intraop parallelism on CPU Input: wav formt file, support formats: `str, np.ndarray, List[str]` Output: `List[str]`: recognition result #### FSMN-VAD-online ```python from funasr_onnx import Fsmn_vad_online import soundfile from pathlib import Path model_dir = "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch" wav_path = '{}/.cache/modelscope/hub/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/example/vad_example.wav'.format(Path.home()) model = Fsmn_vad_online(model_dir) ##online vad speech, sample_rate = soundfile.read(wav_path) speech_length = speech.shape[0] # sample_offset = 0 step = 1600 param_dict = {'in_cache': []} for sample_offset in range(0, speech_length, min(step, speech_length - sample_offset)): if sample_offset + step >= speech_length - 1: step = speech_length - sample_offset is_final = True else: is_final = False param_dict['is_final'] = is_final segments_result = model(audio_in=speech[sample_offset: sample_offset + step], param_dict=param_dict) if segments_result: print(segments_result) ``` - `model_dir`: model_name in modelscope or local path downloaded from modelscope. If the local path is set, it should contain `model.onnx`, `config.yaml`, `am.mvn` - `batch_size`: `1` (Default), the batch size duration inference - `device_id`: `-1` (Default), infer on CPU. If you want to infer with GPU, set it to gpu_id (Please make sure that you have install the onnxruntime-gpu) - `quantize`: `False` (Default), load the model of `model.onnx` in `model_dir`. If set `True`, load the model of `model_quant.onnx` in `model_dir` - `intra_op_num_threads`: `4` (Default), sets the number of threads used for intraop parallelism on CPU Input: wav formt file, support formats: `str, np.ndarray, List[str]` Output: `List[str]`: recognition result ### Punctuation Restoration #### CT-Transformer ```python from funasr_onnx import CT_Transformer model_dir = "damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch" model = CT_Transformer(model_dir) text_in="跨境河流是养育沿岸人民的生命之源长期以来为帮助下游地区防灾减灾中方技术人员在上游地区极为恶劣的自然条件下克服巨大困难甚至冒着生命危险向印方提供汛期水文资料处理紧急事件中方重视印方在跨境河流问题上的关切愿意进一步完善双方联合工作机制凡是中方能做的我们都会去做而且会做得更好我请印度朋友们放心中国在上游的任何开发利用都会经过科学规划和论证兼顾上下游的利益" result = model(text_in) print(result[0]) ``` - `model_dir`: model_name in modelscope or local path downloaded from modelscope. If the local path is set, it should contain `model.onnx`, `config.yaml`, `am.mvn` - `device_id`: `-1` (Default), infer on CPU. If you want to infer with GPU, set it to gpu_id (Please make sure that you have install the onnxruntime-gpu) - `quantize`: `False` (Default), load the model of `model.onnx` in `model_dir`. If set `True`, load the model of `model_quant.onnx` in `model_dir` - `intra_op_num_threads`: `4` (Default), sets the number of threads used for intraop parallelism on CPU Input: `str`, raw text of asr result Output: `List[str]`: recognition result #### CT-Transformer-online ```python from funasr_onnx import CT_Transformer_VadRealtime model_dir = "damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727" model = CT_Transformer_VadRealtime(model_dir) text_in = "跨境河流是养育沿岸|人民的生命之源长期以来为帮助下游地区防灾减灾中方技术人员|在上游地区极为恶劣的自然条件下克服巨大困难甚至冒着生命危险|向印方提供汛期水文资料处理紧急事件中方重视印方在跨境河流>问题上的关切|愿意进一步完善双方联合工作机制|凡是|中方能做的我们|都会去做而且会做得更好我请印度朋友们放心中国在上游的|任何开发利用都会经过科学|规划和论证兼顾上下游的利益" vads = text_in.split("|") rec_result_all="" param_dict = {"cache": []} for vad in vads: result = model(vad, param_dict=param_dict) rec_result_all += result[0] print(rec_result_all) ``` - `model_dir`: model_name in modelscope or local path downloaded from modelscope. If the local path is set, it should contain `model.onnx`, `config.yaml`, `am.mvn` - `device_id`: `-1` (Default), infer on CPU. If you want to infer with GPU, set it to gpu_id (Please make sure that you have install the onnxruntime-gpu) - `quantize`: `False` (Default), load the model of `model.onnx` in `model_dir`. If set `True`, load the model of `model_quant.onnx` in `model_dir` - `intra_op_num_threads`: `4` (Default), sets the number of threads used for intraop parallelism on CPU Input: `str`, raw text of asr result Output: `List[str]`: recognition result ## Performance benchmark Please ref to [benchmark](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/python/benchmark_onnx.md) ## Acknowledge 1. This project is maintained by [FunASR community](https://github.com/alibaba-damo-academy/FunASR). 2. We partially refer [SWHL](https://github.com/RapidAI/RapidASR) for onnxruntime (only for paraformer model). funasr/runtime/python/onnxruntime/funasr_onnx.egg-info/SOURCES.txt
New file @@ -0,0 +1,17 @@ README.md setup.py funasr_onnx/__init__.py funasr_onnx/paraformer_bin.py funasr_onnx/punc_bin.py funasr_onnx/vad_bin.py funasr_onnx.egg-info/PKG-INFO funasr_onnx.egg-info/SOURCES.txt funasr_onnx.egg-info/dependency_links.txt funasr_onnx.egg-info/requires.txt funasr_onnx.egg-info/top_level.txt funasr_onnx/utils/__init__.py funasr_onnx/utils/e2e_vad.py funasr_onnx/utils/frontend.py funasr_onnx/utils/postprocess_utils.py funasr_onnx/utils/timestamp_utils.py funasr_onnx/utils/utils.py funasr/runtime/python/onnxruntime/funasr_onnx.egg-info/dependency_links.txt
New file @@ -0,0 +1 @@
New file @@ -0,0 +1,10 @@ librosa onnxruntime>=1.7.0 scipy numpy>=1.19.3 typeguard kaldi-native-fbank PyYAML>=5.1.2 funasr modelscope onnx funasr/runtime/python/onnxruntime/funasr_onnx.egg-info/top_level.txt
New file @@ -0,0 +1 @@ funasr_onnx funasr/runtime/python/websocket/ws_client.py
@@ -85,9 +85,8 @@ input=True, frames_per_buffer=CHUNK) message = json.dumps({"chunk_size": args.chunk_size, "chunk_interval": args.chunk_interval, "wav_name": wav_name,"is_speaking": True}) message = json.dumps({"chunk_size": args.chunk_size, "chunk_interval": args.chunk_interval, "wav_name": "microphone", "is_speaking": True}) voices.put(message) is_speaking = True while True: data = stream.read(CHUNK) @@ -146,9 +145,6 @@ sleep_duration = 0.001 if args.send_without_sleep else 60*args.chunk_size[1]/args.chunk_interval/1000 await asyncio.sleep(sleep_duration) is_finished = True message = json.dumps({"is_finished": is_finished}) voices.put(message) async def ws_send(): global voices @@ -241,29 +237,9 @@ if __name__ == '__main__': # calculate the number of wavs for each preocess if args.audio_in.endswith(".scp"): f_scp = open(args.audio_in) wavs = f_scp.readlines() else: wavs = [args.audio_in] total_len=len(wavs) if total_len>=args.test_thread_num: chunk_size=int((total_len)/args.test_thread_num) remain_wavs=total_len-chunk_size*args.test_thread_num else: chunk_size=0 process_list = [] chunk_begin=0 for i in range(args.test_thread_num): now_chunk_size= chunk_size if remain_wavs>0: now_chunk_size=chunk_size+1 remain_wavs=remain_wavs-1 # process i handle wavs at chunk_begin and size of now_chunk_size p = Process(target=one_thread,args=(i,chunk_begin,now_chunk_size)) chunk_begin=chunk_begin+now_chunk_size p = Process(target=one_thread,args=(i, 0, 0)) p.start() process_list.append(p) @@ -271,5 +247,38 @@ p.join() print('end') # # if __name__ == '__main__': # # calculate the number of wavs for each preocess # if args.audio_in.endswith(".scp"): # f_scp = open(args.audio_in) # wavs = f_scp.readlines() # else: # wavs = [args.audio_in] # total_len=len(wavs) # if total_len>=args.test_thread_num: # chunk_size=int((total_len)/args.test_thread_num) # remain_wavs=total_len-chunk_size*args.test_thread_num # else: # chunk_size=0 # # process_list = [] # chunk_begin=0 # for i in range(args.test_thread_num): # now_chunk_size= chunk_size # if remain_wavs>0: # now_chunk_size=chunk_size+1 # remain_wavs=remain_wavs-1 # # process i handle wavs at chunk_begin and size of now_chunk_size # p = Process(target=one_thread,args=(i,chunk_begin,now_chunk_size)) # chunk_begin=chunk_begin+now_chunk_size # p.start() # process_list.append(p) # # for i in process_list: # p.join() # # print('end') #
@@ -74,47 +74,54 @@ websocket.param_dict_punc = {'cache': list()} websocket.vad_pre_idx = 0 speech_start = False websocket.wav_name = "microphone" print("new user connected", flush=True) try: async for message in websocket: message = json.loads(message) is_finished = message["is_finished"] if not is_finished: audio = bytes(message['audio'], 'ISO-8859-1') frames.append(audio) duration_ms = len(audio)//32 websocket.vad_pre_idx += duration_ms is_speaking = message["is_speaking"] websocket.param_dict_vad["is_final"] = not is_speaking websocket.param_dict_asr_online["is_final"] = not is_speaking websocket.param_dict_asr_online["chunk_size"] = message["chunk_size"] websocket.wav_name = message.get("wav_name", "demo") # asr online frames_asr_online.append(audio) if len(frames_asr_online) % message["chunk_interval"] == 0: audio_in = b"".join(frames_asr_online) await async_asr_online(websocket, audio_in) frames_asr_online = [] if speech_start: frames_asr.append(audio) # vad online speech_start_i, speech_end_i = await async_vad(websocket, audio) if speech_start_i: speech_start = True beg_bias = (websocket.vad_pre_idx-speech_start_i)//duration_ms frames_pre = frames[-beg_bias:] frames_asr = [] frames_asr.extend(frames_pre) if isinstance(message, str): messagejson = json.loads(message) if "is_speaking" in messagejson: websocket.is_speaking = messagejson["is_speaking"] websocket.param_dict_asr_online["is_final"] = not websocket.is_speaking if "chunk_interval" in messagejson: websocket.chunk_interval = messagejson["chunk_interval"] if "wav_name" in messagejson: websocket.wav_name = messagejson.get("wav_name") if "chunk_size" in messagejson: websocket.param_dict_asr_online["chunk_size"] = messagejson["chunk_size"] if len(frames_asr_online) > 0 or len(frames_asr) > 0 or not isinstance(message, str): if not isinstance(message, str): frames.append(message) duration_ms = len(message)//32 websocket.vad_pre_idx += duration_ms # asr online frames_asr_online.append(message) if len(frames_asr_online) % websocket.chunk_interval == 0: audio_in = b"".join(frames_asr_online) await async_asr_online(websocket, audio_in) frames_asr_online = [] if speech_start: frames_asr.append(message) # vad online speech_start_i, speech_end_i = await async_vad(websocket, message) if speech_start_i: speech_start = True beg_bias = (websocket.vad_pre_idx-speech_start_i)//duration_ms frames_pre = frames[-beg_bias:] frames_asr = [] frames_asr.extend(frames_pre) # asr punc offline if speech_end_i or not is_speaking: if speech_end_i or not websocket.is_speaking: audio_in = b"".join(frames_asr) await async_asr(websocket, audio_in) frames_asr = [] speech_start = False frames_asr_online = [] websocket.param_dict_asr_online = {"cache": dict()} if not is_speaking: if not websocket.is_speaking: websocket.vad_pre_idx = 0 frames = [] websocket.param_dict_vad = {'in_cache': dict()} @@ -168,7 +175,7 @@ audio_in = load_bytes(audio_in) rec_result = inference_pipeline_asr_online(audio_in=audio_in, param_dict=websocket.param_dict_asr_online) if websocket.param_dict_asr_online["is_final"]: if websocket.param_dict_asr_online.get("is_final", False): websocket.param_dict_asr_online["cache"] = dict() if "text" in rec_result: if rec_result["text"] != "sil" and rec_result["text"] != "waiting_for_more_voice": funasr/runtime/python/websocket/ws_server_offline.py
@@ -65,35 +65,40 @@ websocket.param_dict_punc = {'cache': list()} websocket.vad_pre_idx = 0 speech_start = False websocket.wav_name = "microphone" print("new user connected", flush=True) try: async for message in websocket: message = json.loads(message) is_finished = message["is_finished"] if not is_finished: audio = bytes(message['audio'], 'ISO-8859-1') frames.append(audio) duration_ms = len(audio)//32 websocket.vad_pre_idx += duration_ms is_speaking = message["is_speaking"] websocket.param_dict_vad["is_final"] = not is_speaking websocket.wav_name = message.get("wav_name", "demo") if speech_start: frames_asr.append(audio) speech_start_i, speech_end_i = await async_vad(websocket, audio) if speech_start_i: speech_start = True beg_bias = (websocket.vad_pre_idx-speech_start_i)//duration_ms frames_pre = frames[-beg_bias:] frames_asr = [] frames_asr.extend(frames_pre) if speech_end_i or not is_speaking: if isinstance(message, str): messagejson = json.loads(message) if "is_speaking" in messagejson: websocket.is_speaking = messagejson["is_speaking"] websocket.param_dict_vad["is_final"] = not websocket.is_speaking if "wav_name" in messagejson: websocket.wav_name = messagejson.get("wav_name") if len(frames_asr) > 0 or not isinstance(message, str): if not isinstance(message, str): frames.append(message) duration_ms = len(message)//32 websocket.vad_pre_idx += duration_ms if speech_start: frames_asr.append(message) speech_start_i, speech_end_i = await async_vad(websocket, message) if speech_start_i: speech_start = True beg_bias = (websocket.vad_pre_idx-speech_start_i)//duration_ms frames_pre = frames[-beg_bias:] frames_asr = [] frames_asr.extend(frames_pre) if speech_end_i or not websocket.is_speaking: audio_in = b"".join(frames_asr) await async_asr(websocket, audio_in) frames_asr = [] speech_start = False if not is_speaking: if not websocket.is_speaking: websocket.vad_pre_idx = 0 frames = [] websocket.param_dict_vad = {'in_cache': dict()} @@ -133,7 +138,7 @@ rec_result = inference_pipeline_asr(audio_in=audio_in, param_dict=websocket.param_dict_asr) # print(rec_result) print(rec_result) if inference_pipeline_punc is not None and 'text' in rec_result and len(rec_result["text"])>0: rec_result = inference_pipeline_punc(text_in=rec_result['text'], param_dict=websocket.param_dict_punc) funasr/runtime/python/websocket/ws_server_online.py
@@ -26,74 +26,72 @@ print("model loading") inference_pipeline_asr_online = pipeline( task=Tasks.auto_speech_recognition, model=args.asr_model_online, ngpu=args.ngpu, ncpu=args.ncpu, model_revision='v1.0.4') task=Tasks.auto_speech_recognition, model=args.asr_model_online, ngpu=args.ngpu, ncpu=args.ncpu, model_revision='v1.0.4') print("model loaded") async def ws_serve(websocket, path): frames_asr_online = [] global websocket_users websocket_users.add(websocket) websocket.param_dict_asr_online = {"cache": dict()} print("new user connected",flush=True) try: async for message in websocket: if isinstance(message,str): messagejson = json.loads(message) if "is_speaking" in messagejson: websocket.is_speaking = messagejson["is_speaking"] websocket.param_dict_asr_online["is_final"] = not websocket.is_speaking if "is_finished" in messagejson: websocket.is_speaking = False websocket.param_dict_asr_online["is_final"] = True if "chunk_interval" in messagejson: websocket.chunk_interval=messagejson["chunk_interval"] if "wav_name" in messagejson: websocket.wav_name = messagejson.get("wav_name", "demo") if "chunk_size" in messagejson: websocket.param_dict_asr_online["chunk_size"] = messagejson["chunk_size"] # if has bytes in buffer or message is bytes if len(frames_asr_online)>0 or not isinstance(message,str): if not isinstance(message,str): frames_asr_online.append(message) if len(frames_asr_online) % websocket.chunk_interval == 0 or not websocket.is_speaking: audio_in = b"".join(frames_asr_online) if not websocket.is_speaking: #padding 0.5s at end gurantee that asr engine can fire out last word audio_in=audio_in+b''.join(np.zeros(int(16000*0.5),dtype=np.int16)) await async_asr_online(websocket,audio_in) frames_asr_online = [] frames_asr_online = [] global websocket_users websocket_users.add(websocket) websocket.param_dict_asr_online = {"cache": dict()} websocket.wav_name = "microphone" print("new user connected",flush=True) try: async for message in websocket: if isinstance(message, str): messagejson = json.loads(message) if "is_speaking" in messagejson: websocket.is_speaking = messagejson["is_speaking"] websocket.param_dict_asr_online["is_final"] = not websocket.is_speaking if "chunk_interval" in messagejson: websocket.chunk_interval=messagejson["chunk_interval"] if "wav_name" in messagejson: websocket.wav_name = messagejson.get("wav_name") if "chunk_size" in messagejson: websocket.param_dict_asr_online["chunk_size"] = messagejson["chunk_size"] # if has bytes in buffer or message is bytes if len(frames_asr_online) > 0 or not isinstance(message, str): if not isinstance(message,str): frames_asr_online.append(message) if len(frames_asr_online) % websocket.chunk_interval == 0 or not websocket.is_speaking: audio_in = b"".join(frames_asr_online) # if not websocket.is_speaking: #padding 0.5s at end gurantee that asr engine can fire out last word # audio_in=audio_in+b''.join(np.zeros(int(16000*0.5),dtype=np.int16)) await async_asr_online(websocket,audio_in) frames_asr_online = [] except websockets.ConnectionClosed: print("ConnectionClosed...", websocket_users) websocket_users.remove(websocket) except websockets.InvalidState: print("InvalidState...") except Exception as e: print("Exception:", e) except websockets.ConnectionClosed: print("ConnectionClosed...", websocket_users) websocket_users.remove(websocket) except websockets.InvalidState: print("InvalidState...") except Exception as e: print("Exception:", e) async def async_asr_online(websocket,audio_in): if len(audio_in) > 0: audio_in = load_bytes(audio_in) rec_result = inference_pipeline_asr_online(audio_in=audio_in, param_dict=websocket.param_dict_asr_online) if websocket.param_dict_asr_online["is_final"]: websocket.param_dict_asr_online["cache"] = dict() if "text" in rec_result: if rec_result["text"] != "sil" and rec_result["text"] != "waiting_for_more_voice": message = json.dumps({"mode": "online", "text": rec_result["text"], "wav_name": websocket.wav_name}) await websocket.send(message) if len(audio_in) > 0: audio_in = load_bytes(audio_in) rec_result = inference_pipeline_asr_online(audio_in=audio_in, param_dict=websocket.param_dict_asr_online) if websocket.param_dict_asr_online.get("is_final", False): websocket.param_dict_asr_online["cache"] = dict() if "text" in rec_result: if rec_result["text"] != "sil" and rec_result["text"] != "waiting_for_more_voice": message = json.dumps({"mode": "online", "text": rec_result["text"], "wav_name": websocket.wav_name}) await websocket.send(message)
New file @@ -0,0 +1,16 @@ import os from modelscope.hub.snapshot_download import snapshot_download def check_model_dir(model_dir, model_name: str = "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"): model_dir = "/Users/zhifu/test_modelscope_pipeline/FSMN-VAD" cache_root = os.path.dirname(model_dir) dst_dir_root = os.path.join(cache_root, ".cache") dst = os.path.join(dst_dir_root, model_name) dst_dir = os.path.dirname(dst) os.makedirs(dst_dir, exist_ok=True) if not os.path.exists(dst): os.symlink(model_dir, dst) model_dir = snapshot_download(model_name, cache_dir=dst_dir_root)
