From 244c033fbaeae15faf8b0351365bdb7607b2e2bb Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 17 十一月 2023 15:19:53 +0800
Subject: [PATCH] python cli
---
funasr/__init__.py | 127 ------------------------------------------
1 files changed, 1 insertions(+), 126 deletions(-)
diff --git a/funasr/__init__.py b/funasr/__init__.py
index aab4289..d0b7aa5 100644
--- a/funasr/__init__.py
+++ b/funasr/__init__.py
@@ -1,135 +1,10 @@
"""Initialize funasr package."""
import os
-from pathlib import Path
-import torch
-import numpy as np
dirname = os.path.dirname(__file__)
version_file = os.path.join(dirname, "version.txt")
with open(version_file, "r") as f:
__version__ = f.read().strip()
-
-def prepare_model(
- model: str = None,
- # mode: str = None,
- vad_model: str = None,
- punc_model: str = None,
- model_hub: str = "ms",
- cache_dir: str = None,
- **kwargs,
-):
- if not Path(model).exists():
- if model_hub == "ms" or model_hub == "modelscope":
- try:
- from modelscope.hub.snapshot_download import snapshot_download as download_tool
- model = name_maps_ms[model] if model is not None else None
- vad_model = name_maps_ms[vad_model] if vad_model is not None else None
- punc_model = name_maps_ms[punc_model] if punc_model is not None else None
- except:
- raise "You are exporting model from modelscope, please install modelscope and try it again. To install modelscope, you could:\n" \
- "\npip3 install -U modelscope\n" \
- "For the users in China, you could install with the command:\n" \
- "\npip3 install -U modelscope -i https://mirror.sjtu.edu.cn/pypi/web/simple"
- elif model_hub == "hf" or model_hub == "huggingface":
- download_tool = 0
- else:
- raise "model_hub must be on of ms or hf, but get {}".format(model_hub)
- try:
- model = download_tool(model, cache_dir=cache_dir, revision=kwargs.get("revision", None))
- print("model have been downloaded to: {}".format(model))
- except:
- raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format(
- model)
-
- if vad_model is not None and not Path(vad_model).exists():
- vad_model = download_tool(vad_model, cache_dir=cache_dir)
- print("model have been downloaded to: {}".format(vad_model))
- if punc_model is not None and not Path(punc_model).exists():
- punc_model = download_tool(punc_model, cache_dir=cache_dir)
- print("model have been downloaded to: {}".format(punc_model))
-
- # asr
- kwargs.update({"cmvn_file": None if model is None else os.path.join(model, "am.mvn"),
- "asr_model_file": None if model is None else os.path.join(model, "model.pb"),
- "asr_train_config": None if model is None else os.path.join(model, "config.yaml"),
- })
- mode = kwargs.get("mode", None)
- if mode is None:
- import json
- json_file = os.path.join(model, 'configuration.json')
- with open(json_file, 'r') as f:
- config_data = json.load(f)
- if config_data['task'] == "punctuation":
- mode = config_data['model']['punc_model_config']['mode']
- else:
- mode = config_data['model']['model_config']['mode']
- if vad_model is not None and "vad" not in mode:
- mode = "paraformer_vad"
- kwargs["mode"] = mode
- # vad
- kwargs.update({"vad_cmvn_file": None if vad_model is None else os.path.join(vad_model, "vad.mvn"),
- "vad_model_file": None if vad_model is None else os.path.join(vad_model, "vad.pb"),
- "vad_infer_config": None if vad_model is None else os.path.join(vad_model, "vad.yaml"),
- })
- # punc
- kwargs.update({
- "punc_model_file": None if punc_model is None else os.path.join(punc_model, "punc.pb"),
- "punc_infer_config": None if punc_model is None else os.path.join(punc_model, "punc.yaml"),
- })
-
-
- return model, vad_model, punc_model, kwargs
-
-name_maps_ms = {
- "paraformer-zh": "damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
- "paraformer-zh-spk": "damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn",
- "paraformer-en": "damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020",
- "paraformer-en-spk": "damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020",
- "paraformer-zh-streaming": "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online",
- "fsmn-vad": "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
- "ct-punc": "damo/punc_ct-transformer_cn-en-common-vocab471067-large",
- "fa-zh": "damo/speech_timestamp_prediction-v1-16k-offline",
-}
-
-def infer(task_name: str = "asr",
- model: str = None,
- # mode: str = None,
- vad_model: str = None,
- punc_model: str = None,
- model_hub: str = "ms",
- cache_dir: str = None,
- **kwargs,
- ):
-
- model, vad_model, punc_model, kwargs = prepare_model(model, vad_model, punc_model, model_hub, cache_dir, **kwargs)
- if task_name == "asr":
- from funasr.bin.asr_inference_launch import inference_launch
-
- inference_pipeline = inference_launch(**kwargs)
- elif task_name == "":
- pipeline = 1
- elif task_name == "":
- pipeline = 2
- elif task_name == "":
- pipeline = 2
-
- def _infer_fn(input, **kwargs):
- data_type = kwargs.get('data_type', 'sound')
- data_path_and_name_and_type = [input, 'speech', data_type]
- raw_inputs = None
- if isinstance(input, torch.Tensor):
- input = input.numpy()
- if isinstance(input, np.ndarray):
- data_path_and_name_and_type = None
- raw_inputs = input
-
-
-
- return inference_pipeline(data_path_and_name_and_type, raw_inputs=raw_inputs, **kwargs)
-
- return _infer_fn
-
-if __name__ == '__main__':
- pass
\ No newline at end of file
+from funasr.bin.inference_cli import infer
\ No newline at end of file
--
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