mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
35b1c051f6
* update * update * update * update onnx * update with main (#1492) * contextual&seaco ONNX export (#1481) * contextual&seaco ONNX export * update ContextualEmbedderExport2 * update ContextualEmbedderExport2 * update code * onnx (#1482) * qwenaudio qwenaudiochat * qwenaudio qwenaudiochat * whisper * whisper * llm * llm * llm * llm * llm * llm * llm * llm * export onnx * export onnx * export onnx * dingding * dingding * llm * doc * onnx * onnx * onnx * onnx * onnx * onnx * v1.0.15 * qwenaudio * qwenaudio * issue doc * update * update * bugfix * onnx * update export calling * update codes * remove useless code * update code --------- Co-authored-by: zhifu gao <zhifu.gzf@alibaba-inc.com> * acknowledge --------- Co-authored-by: Shi Xian <40013335+R1ckShi@users.noreply.github.com> * update onnx * update onnx --------- Co-authored-by: Shi Xian <40013335+R1ckShi@users.noreply.github.com>
333 lines
12 KiB
Python
333 lines
12 KiB
Python
# -*- encoding: utf-8 -*-
|
|
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
|
|
# MIT License (https://opensource.org/licenses/MIT)
|
|
|
|
import os.path
|
|
from pathlib import Path
|
|
from typing import List, Union, Tuple
|
|
|
|
import copy
|
|
import librosa
|
|
import numpy as np
|
|
|
|
from .utils.utils import (ONNXRuntimeError,
|
|
OrtInferSession, get_logger,
|
|
read_yaml)
|
|
from .utils.frontend import WavFrontend, WavFrontendOnline
|
|
from .utils.e2e_vad import E2EVadModel
|
|
|
|
logging = get_logger()
|
|
|
|
|
|
class Fsmn_vad():
|
|
"""
|
|
Author: Speech Lab of DAMO Academy, Alibaba Group
|
|
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
|
|
https://arxiv.org/abs/1803.05030
|
|
"""
|
|
def __init__(self, model_dir: Union[str, Path] = None,
|
|
batch_size: int = 1,
|
|
device_id: Union[str, int] = "-1",
|
|
quantize: bool = False,
|
|
intra_op_num_threads: int = 4,
|
|
max_end_sil: int = None,
|
|
cache_dir: str = None,
|
|
**kwargs
|
|
):
|
|
|
|
if not Path(model_dir).exists():
|
|
try:
|
|
from modelscope.hub.snapshot_download import snapshot_download
|
|
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"
|
|
try:
|
|
model_dir = snapshot_download(model_dir, cache_dir=cache_dir)
|
|
except:
|
|
raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format(
|
|
model_dir)
|
|
|
|
model_file = os.path.join(model_dir, 'model.onnx')
|
|
if quantize:
|
|
model_file = os.path.join(model_dir, 'model_quant.onnx')
|
|
if not os.path.exists(model_file):
|
|
print(".onnx is not exist, begin to export onnx")
|
|
try:
|
|
from funasr import AutoModel
|
|
except:
|
|
raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" \
|
|
"\npip3 install -U funasr\n" \
|
|
"For the users in China, you could install with the command:\n" \
|
|
"\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple"
|
|
|
|
model = AutoModel(model=model_dir)
|
|
model_dir = model.export(type="onnx", quantize=quantize, **kwargs)
|
|
config_file = os.path.join(model_dir, 'config.yaml')
|
|
cmvn_file = os.path.join(model_dir, 'am.mvn')
|
|
config = read_yaml(config_file)
|
|
|
|
self.frontend = WavFrontend(
|
|
cmvn_file=cmvn_file,
|
|
**config['frontend_conf']
|
|
)
|
|
self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
|
|
self.batch_size = batch_size
|
|
self.vad_scorer = E2EVadModel(config["model_conf"])
|
|
self.max_end_sil = max_end_sil if max_end_sil is not None else config["model_conf"]["max_end_silence_time"]
|
|
self.encoder_conf = config["encoder_conf"]
|
|
|
|
def prepare_cache(self, in_cache: list = []):
|
|
if len(in_cache) > 0:
|
|
return in_cache
|
|
fsmn_layers = self.encoder_conf["fsmn_layers"]
|
|
proj_dim = self.encoder_conf["proj_dim"]
|
|
lorder = self.encoder_conf["lorder"]
|
|
for i in range(fsmn_layers):
|
|
cache = np.zeros((1, proj_dim, lorder-1, 1)).astype(np.float32)
|
|
in_cache.append(cache)
|
|
return in_cache
|
|
|
|
|
|
def __call__(self, audio_in: Union[str, np.ndarray, List[str]], **kwargs) -> List:
|
|
waveform_list = self.load_data(audio_in, self.frontend.opts.frame_opts.samp_freq)
|
|
waveform_nums = len(waveform_list)
|
|
is_final = kwargs.get('kwargs', False)
|
|
|
|
segments = [[]] * self.batch_size
|
|
for beg_idx in range(0, waveform_nums, self.batch_size):
|
|
|
|
end_idx = min(waveform_nums, beg_idx + self.batch_size)
|
|
waveform = waveform_list[beg_idx:end_idx]
|
|
feats, feats_len = self.extract_feat(waveform)
|
|
waveform = np.array(waveform)
|
|
param_dict = kwargs.get('param_dict', dict())
|
|
in_cache = param_dict.get('in_cache', list())
|
|
in_cache = self.prepare_cache(in_cache)
|
|
try:
|
|
t_offset = 0
|
|
step = int(min(feats_len.max(), 6000))
|
|
for t_offset in range(0, int(feats_len), min(step, feats_len - t_offset)):
|
|
if t_offset + step >= feats_len - 1:
|
|
step = feats_len - t_offset
|
|
is_final = True
|
|
else:
|
|
is_final = False
|
|
feats_package = feats[:, t_offset:int(t_offset + step), :]
|
|
waveform_package = waveform[:, t_offset * 160:min(waveform.shape[-1], (int(t_offset + step) - 1) * 160 + 400)]
|
|
|
|
inputs = [feats_package]
|
|
# inputs = [feats]
|
|
inputs.extend(in_cache)
|
|
scores, out_caches = self.infer(inputs)
|
|
in_cache = out_caches
|
|
segments_part = self.vad_scorer(scores, waveform_package, is_final=is_final, max_end_sil=self.max_end_sil, online=False)
|
|
# segments = self.vad_scorer(scores, waveform[0][None, :], is_final=is_final, max_end_sil=self.max_end_sil)
|
|
|
|
if segments_part:
|
|
for batch_num in range(0, self.batch_size):
|
|
segments[batch_num] += segments_part[batch_num]
|
|
|
|
except ONNXRuntimeError:
|
|
# logging.warning(traceback.format_exc())
|
|
logging.warning("input wav is silence or noise")
|
|
segments = ''
|
|
|
|
return segments
|
|
|
|
def load_data(self,
|
|
wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
|
|
def load_wav(path: str) -> np.ndarray:
|
|
waveform, _ = librosa.load(path, sr=fs)
|
|
return waveform
|
|
|
|
if isinstance(wav_content, np.ndarray):
|
|
return [wav_content]
|
|
|
|
if isinstance(wav_content, str):
|
|
return [load_wav(wav_content)]
|
|
|
|
if isinstance(wav_content, list):
|
|
return [load_wav(path) for path in wav_content]
|
|
|
|
raise TypeError(
|
|
f'The type of {wav_content} is not in [str, np.ndarray, list]')
|
|
|
|
def extract_feat(self,
|
|
waveform_list: List[np.ndarray]
|
|
) -> Tuple[np.ndarray, np.ndarray]:
|
|
feats, feats_len = [], []
|
|
for waveform in waveform_list:
|
|
speech, _ = self.frontend.fbank(waveform)
|
|
feat, feat_len = self.frontend.lfr_cmvn(speech)
|
|
feats.append(feat)
|
|
feats_len.append(feat_len)
|
|
|
|
feats = self.pad_feats(feats, np.max(feats_len))
|
|
feats_len = np.array(feats_len).astype(np.int32)
|
|
return feats, feats_len
|
|
|
|
@staticmethod
|
|
def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray:
|
|
def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray:
|
|
pad_width = ((0, max_feat_len - cur_len), (0, 0))
|
|
return np.pad(feat, pad_width, 'constant', constant_values=0)
|
|
|
|
feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats]
|
|
feats = np.array(feat_res).astype(np.float32)
|
|
return feats
|
|
|
|
def infer(self, feats: List) -> Tuple[np.ndarray, np.ndarray]:
|
|
|
|
outputs = self.ort_infer(feats)
|
|
scores, out_caches = outputs[0], outputs[1:]
|
|
return scores, out_caches
|
|
|
|
|
|
class Fsmn_vad_online():
|
|
"""
|
|
Author: Speech Lab of DAMO Academy, Alibaba Group
|
|
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
|
|
https://arxiv.org/abs/1803.05030
|
|
"""
|
|
def __init__(self, model_dir: Union[str, Path] = None,
|
|
batch_size: int = 1,
|
|
device_id: Union[str, int] = "-1",
|
|
quantize: bool = False,
|
|
intra_op_num_threads: int = 4,
|
|
max_end_sil: int = None,
|
|
cache_dir: str = None,
|
|
**kwargs
|
|
):
|
|
if not Path(model_dir).exists():
|
|
try:
|
|
from modelscope.hub.snapshot_download import snapshot_download
|
|
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"
|
|
try:
|
|
model_dir = snapshot_download(model_dir, cache_dir=cache_dir)
|
|
except:
|
|
raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format(
|
|
model_dir)
|
|
|
|
model_file = os.path.join(model_dir, 'model.onnx')
|
|
if quantize:
|
|
model_file = os.path.join(model_dir, 'model_quant.onnx')
|
|
if not os.path.exists(model_file):
|
|
print(".onnx is not exist, begin to export onnx")
|
|
try:
|
|
from funasr import AutoModel
|
|
except:
|
|
raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" \
|
|
"\npip3 install -U funasr\n" \
|
|
"For the users in China, you could install with the command:\n" \
|
|
"\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple"
|
|
|
|
model = AutoModel(model=model_dir)
|
|
model_dir = model.export(type="onnx", quantize=quantize, **kwargs)
|
|
|
|
config_file = os.path.join(model_dir, 'config.yaml')
|
|
cmvn_file = os.path.join(model_dir, 'am.mvn')
|
|
config = read_yaml(config_file)
|
|
|
|
self.frontend = WavFrontendOnline(
|
|
cmvn_file=cmvn_file,
|
|
**config['frontend_conf']
|
|
)
|
|
self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
|
|
self.batch_size = batch_size
|
|
self.vad_scorer = E2EVadModel(config["model_conf"])
|
|
self.max_end_sil = max_end_sil if max_end_sil is not None else config["model_conf"]["max_end_silence_time"]
|
|
self.encoder_conf = config["encoder_conf"]
|
|
|
|
def prepare_cache(self, in_cache: list = []):
|
|
if len(in_cache) > 0:
|
|
return in_cache
|
|
fsmn_layers = self.encoder_conf["fsmn_layers"]
|
|
proj_dim = self.encoder_conf["proj_dim"]
|
|
lorder = self.encoder_conf["lorder"]
|
|
for i in range(fsmn_layers):
|
|
cache = np.zeros((1, proj_dim, lorder - 1, 1)).astype(np.float32)
|
|
in_cache.append(cache)
|
|
return in_cache
|
|
|
|
def __call__(self, audio_in: np.ndarray, **kwargs) -> List:
|
|
waveforms = np.expand_dims(audio_in, axis=0)
|
|
|
|
param_dict = kwargs.get('param_dict', dict())
|
|
is_final = param_dict.get('is_final', False)
|
|
feats, feats_len = self.extract_feat(waveforms, is_final)
|
|
segments = []
|
|
if feats.size != 0:
|
|
in_cache = param_dict.get('in_cache', list())
|
|
in_cache = self.prepare_cache(in_cache)
|
|
try:
|
|
inputs = [feats]
|
|
inputs.extend(in_cache)
|
|
scores, out_caches = self.infer(inputs)
|
|
param_dict['in_cache'] = out_caches
|
|
waveforms = self.frontend.get_waveforms()
|
|
segments = self.vad_scorer(scores, waveforms, is_final=is_final, max_end_sil=self.max_end_sil,
|
|
online=True)
|
|
|
|
|
|
except ONNXRuntimeError:
|
|
# logging.warning(traceback.format_exc())
|
|
logging.warning("input wav is silence or noise")
|
|
segments = []
|
|
return segments
|
|
|
|
def load_data(self,
|
|
wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
|
|
def load_wav(path: str) -> np.ndarray:
|
|
waveform, _ = librosa.load(path, sr=fs)
|
|
return waveform
|
|
|
|
if isinstance(wav_content, np.ndarray):
|
|
return [wav_content]
|
|
|
|
if isinstance(wav_content, str):
|
|
return [load_wav(wav_content)]
|
|
|
|
if isinstance(wav_content, list):
|
|
return [load_wav(path) for path in wav_content]
|
|
|
|
raise TypeError(
|
|
f'The type of {wav_content} is not in [str, np.ndarray, list]')
|
|
|
|
def extract_feat(self,
|
|
waveforms: np.ndarray, is_final: bool = False
|
|
) -> Tuple[np.ndarray, np.ndarray]:
|
|
waveforms_lens = np.zeros(waveforms.shape[0]).astype(np.int32)
|
|
for idx, waveform in enumerate(waveforms):
|
|
waveforms_lens[idx] = waveform.shape[-1]
|
|
|
|
feats, feats_len = self.frontend.extract_fbank(waveforms, waveforms_lens, is_final)
|
|
# feats.append(feat)
|
|
# feats_len.append(feat_len)
|
|
|
|
# feats = self.pad_feats(feats, np.max(feats_len))
|
|
# feats_len = np.array(feats_len).astype(np.int32)
|
|
return feats.astype(np.float32), feats_len.astype(np.int32)
|
|
|
|
@staticmethod
|
|
def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray:
|
|
def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray:
|
|
pad_width = ((0, max_feat_len - cur_len), (0, 0))
|
|
return np.pad(feat, pad_width, 'constant', constant_values=0)
|
|
|
|
feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats]
|
|
feats = np.array(feat_res).astype(np.float32)
|
|
return feats
|
|
|
|
def infer(self, feats: List) -> Tuple[np.ndarray, np.ndarray]:
|
|
|
|
outputs = self.ort_infer(feats)
|
|
scores, out_caches = outputs[0], outputs[1:]
|
|
return scores, out_caches
|
|
|