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Merge pull request #129 from alibaba-damo-academy/dev_zly

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zhifu gao 2023-02-17 10:32:12 +08:00 committed by GitHub
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0
egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common-pytorch/README.md → egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common/README.md
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2
egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common-pytorch/infer.py → egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common/infer.py Executable file → Normal file
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@ -5,7 +5,7 @@ if __name__ == '__main__':
audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav'
output_dir = None
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
task=Tasks.voice_activity_detection,
model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
model_revision=None,
output_dir=output_dir,

24
egs_modelscope/vad/speech_fsmn_vad_zh-cn-8k-common/README.md Normal file
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@ -0,0 +1,24 @@
# ModelScope Model
## How to finetune and infer using a pretrained ModelScope Model
### Inference
Or you can use the finetuned model for inference directly.
- Setting parameters in `infer.py`
- <strong>audio_in:</strong> # support wav, url, bytes, and parsed audio format.
- <strong>output_dir:</strong> # If the input format is wav.scp, it needs to be set.
- Then you can run the pipeline to infer with:
```python
python infer.py
```
Modify inference related parameters in vad.yaml.
- max_end_silence_time: The end-point silence duration to judge the end of sentence, the parameter range is 500ms~6000ms, and the default value is 800ms
- speech_noise_thres: The balance of speech and silence scores, the parameter range is (-1,1)
- The value tends to -1, the greater probability of noise being judged as speech
- The value tends to 1, the greater probability of speech being judged as noise

15
egs_modelscope/vad/speech_fsmn_vad_zh-cn-8k-common/infer.py Normal file
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@ -0,0 +1,15 @@
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
if __name__ == '__main__':
audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example_8k.wav'
output_dir = None
inference_pipline = pipeline(
task=Tasks.voice_activity_detection,
model="damo/speech_fsmn_vad_zh-cn-8k-common",
model_revision='v1.1.1',
output_dir='./output_dir',
batch_size=1,
)
segments_result = inference_pipline(audio_in=audio_in)
print(segments_result)

236
funasr/bin/asr_inference_paraformer_vad_punc.py
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@ -144,7 +144,7 @@ class Speech2Text:
for scorer in scorers.values():
if isinstance(scorer, torch.nn.Module):
scorer.to(device=device, dtype=getattr(torch, dtype)).eval()
logging.info(f"Decoding device={device}, dtype={dtype}")
# 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
@ -184,12 +184,11 @@ class Speech2Text:
self.encoder_downsampling_factor = 1
if asr_train_args.encoder_conf["input_layer"] == "conv2d":
self.encoder_downsampling_factor = 4
@torch.no_grad()
def __call__(
self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None, begin_time: int = 0, end_time: int = None,
self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
begin_time: int = 0, end_time: int = None,
):
"""Inference
@ -215,7 +214,7 @@ class Speech2Text:
else:
feats = speech
feats_len = speech_lengths
lfr_factor = max(1, (feats.size()[-1]//80)-1)
lfr_factor = max(1, (feats.size()[-1] // 80) - 1)
batch = {"speech": feats, "speech_lengths": feats_len}
# a. To device
@ -229,7 +228,8 @@ class Speech2Text:
enc_len_batch_total = torch.sum(enc_len).item() * self.encoder_downsampling_factor
predictor_outs = self.asr_model.calc_predictor(enc, enc_len)
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], predictor_outs[2], predictor_outs[3]
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
predictor_outs[2], predictor_outs[3]
pre_token_length = pre_token_length.round().long()
if torch.max(pre_token_length) < 1:
return []
@ -249,7 +249,7 @@ class Speech2Text:
nbest_hyps = self.beam_search(
x=x, am_scores=am_scores, maxlenratio=self.maxlenratio, minlenratio=self.minlenratio
)
nbest_hyps = nbest_hyps[: self.nbest]
else:
yseq = am_scores.argmax(dim=-1)
@ -260,34 +260,37 @@ class Speech2Text:
[self.asr_model.sos] + yseq.tolist() + [self.asr_model.eos], device=yseq.device
)
nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
for hyp in nbest_hyps:
assert isinstance(hyp, (Hypothesis)), type(hyp)
# remove sos/eos and get results
last_pos = -1
if isinstance(hyp.yseq, list):
token_int = hyp.yseq[1:last_pos]
else:
token_int = hyp.yseq[1:last_pos].tolist()
# remove blank symbol id, which is assumed to be 0
token_int = list(filter(lambda x: x != 0 and x != 2, token_int))
# Change integer-ids to tokens
token = self.converter.ids2tokens(token_int)
if self.tokenizer is not None:
text = self.tokenizer.tokens2text(token)
else:
text = None
timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))
# assert check_return_type(results)
return results
class Speech2VadSegment:
"""Speech2VadSegment class
@ -329,6 +332,7 @@ class Speech2VadSegment:
self.device = device
self.dtype = dtype
self.frontend = frontend
self.batch_size = batch_size
@torch.no_grad()
def __call__(
@ -357,56 +361,69 @@ class Speech2VadSegment:
feats_len = feats_len.int()
else:
raise Exception("Need to extract feats first, please configure frontend configuration")
batch = {"feats": feats, "feats_lengths": feats_len, "waveform": speech}
# a. To device
batch = to_device(batch, device=self.device)
# b. Forward Encoder
segments = self.vad_model(**batch)
# b. Forward Encoder streaming
t_offset = 0
step = min(feats_len, 6000)
segments = [[]] * self.batch_size
for t_offset in range(0, feats_len, min(step, feats_len - t_offset)):
if t_offset + step >= feats_len - 1:
step = feats_len - t_offset
is_final_send = True
else:
is_final_send = False
batch = {
"feats": feats[:, t_offset:t_offset + step, :],
"waveform": speech[:, t_offset * 160:min(speech.shape[-1], (t_offset + step - 1) * 160 + 400)],
"is_final_send": is_final_send
}
# a. To device
batch = to_device(batch, device=self.device)
segments_part = self.vad_model(**batch)
if segments_part:
for batch_num in range(0, self.batch_size):
segments[batch_num] += segments_part[batch_num]
return fbanks, segments
def inference(
maxlenratio: float,
minlenratio: float,
batch_size: int,
beam_size: int,
ngpu: int,
ctc_weight: float,
lm_weight: float,
penalty: float,
log_level: Union[int, str],
data_path_and_name_and_type,
asr_train_config: Optional[str],
asr_model_file: Optional[str],
cmvn_file: Optional[str] = None,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
lm_train_config: Optional[str] = None,
lm_file: Optional[str] = None,
token_type: Optional[str] = None,
key_file: Optional[str] = None,
word_lm_train_config: Optional[str] = None,
bpemodel: Optional[str] = None,
allow_variable_data_keys: bool = False,
streaming: bool = False,
output_dir: Optional[str] = None,
dtype: str = "float32",
seed: int = 0,
ngram_weight: float = 0.9,
nbest: int = 1,
num_workers: int = 1,
vad_infer_config: Optional[str] = None,
vad_model_file: Optional[str] = None,
vad_cmvn_file: Optional[str] = None,
time_stamp_writer: bool = False,
punc_infer_config: Optional[str] = None,
punc_model_file: Optional[str] = None,
**kwargs,
maxlenratio: float,
minlenratio: float,
batch_size: int,
beam_size: int,
ngpu: int,
ctc_weight: float,
lm_weight: float,
penalty: float,
log_level: Union[int, str],
data_path_and_name_and_type,
asr_train_config: Optional[str],
asr_model_file: Optional[str],
cmvn_file: Optional[str] = None,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
lm_train_config: Optional[str] = None,
lm_file: Optional[str] = None,
token_type: Optional[str] = None,
key_file: Optional[str] = None,
word_lm_train_config: Optional[str] = None,
bpemodel: Optional[str] = None,
allow_variable_data_keys: bool = False,
streaming: bool = False,
output_dir: Optional[str] = None,
dtype: str = "float32",
seed: int = 0,
ngram_weight: float = 0.9,
nbest: int = 1,
num_workers: int = 1,
vad_infer_config: Optional[str] = None,
vad_model_file: Optional[str] = None,
vad_cmvn_file: Optional[str] = None,
time_stamp_writer: bool = False,
punc_infer_config: Optional[str] = None,
punc_model_file: Optional[str] = None,
**kwargs,
):
inference_pipeline = inference_modelscope(
maxlenratio=maxlenratio,
minlenratio=minlenratio,
@ -445,63 +462,64 @@ def inference(
)
return inference_pipeline(data_path_and_name_and_type, raw_inputs)
def inference_modelscope(
maxlenratio: float,
minlenratio: float,
batch_size: int,
beam_size: int,
ngpu: int,
ctc_weight: float,
lm_weight: float,
penalty: float,
log_level: Union[int, str],
# data_path_and_name_and_type,
asr_train_config: Optional[str],
asr_model_file: Optional[str],
cmvn_file: Optional[str] = None,
lm_train_config: Optional[str] = None,
lm_file: Optional[str] = None,
token_type: Optional[str] = None,
key_file: Optional[str] = None,
word_lm_train_config: Optional[str] = None,
bpemodel: Optional[str] = None,
allow_variable_data_keys: bool = False,
output_dir: Optional[str] = None,
dtype: str = "float32",
seed: int = 0,
ngram_weight: float = 0.9,
nbest: int = 1,
num_workers: int = 1,
vad_infer_config: Optional[str] = None,
vad_model_file: Optional[str] = None,
vad_cmvn_file: Optional[str] = None,
time_stamp_writer: bool = True,
punc_infer_config: Optional[str] = None,
punc_model_file: Optional[str] = None,
outputs_dict: Optional[bool] = True,
param_dict: dict = None,
**kwargs,
maxlenratio: float,
minlenratio: float,
batch_size: int,
beam_size: int,
ngpu: int,
ctc_weight: float,
lm_weight: float,
penalty: float,
log_level: Union[int, str],
# data_path_and_name_and_type,
asr_train_config: Optional[str],
asr_model_file: Optional[str],
cmvn_file: Optional[str] = None,
lm_train_config: Optional[str] = None,
lm_file: Optional[str] = None,
token_type: Optional[str] = None,
key_file: Optional[str] = None,
word_lm_train_config: Optional[str] = None,
bpemodel: Optional[str] = None,
allow_variable_data_keys: bool = False,
output_dir: Optional[str] = None,
dtype: str = "float32",
seed: int = 0,
ngram_weight: float = 0.9,
nbest: int = 1,
num_workers: int = 1,
vad_infer_config: Optional[str] = None,
vad_model_file: Optional[str] = None,
vad_cmvn_file: Optional[str] = None,
time_stamp_writer: bool = True,
punc_infer_config: Optional[str] = None,
punc_model_file: Optional[str] = None,
outputs_dict: Optional[bool] = True,
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
if word_lm_train_config is not None:
raise NotImplementedError("Word LM is not implemented")
if ngpu > 1:
raise NotImplementedError("only single GPU decoding is supported")
logging.basicConfig(
level=log_level,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
if ngpu >= 1 and torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
# 1. Set random-seed
set_all_random_seed(seed)
# 2. Build speech2vadsegment
speech2vadsegment_kwargs = dict(
vad_infer_config=vad_infer_config,
@ -512,7 +530,7 @@ def inference_modelscope(
)
# logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
# 3. Build speech2text
speech2text_kwargs = dict(
asr_train_config=asr_train_config,
@ -535,14 +553,14 @@ def inference_modelscope(
)
speech2text = Speech2Text(**speech2text_kwargs)
text2punc = None
if punc_model_file is not None:
if punc_model_file is not None:
text2punc = Text2Punc(punc_infer_config, punc_model_file, device=device, dtype=dtype)
if output_dir is not None:
writer = DatadirWriter(output_dir)
ibest_writer = writer[f"1best_recog"]
ibest_writer["token_list"][""] = " ".join(speech2text.asr_train_args.token_list)
def _forward(data_path_and_name_and_type,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
output_dir_v2: Optional[str] = None,
@ -571,7 +589,7 @@ def inference_modelscope(
use_timestamp = param_dict.get('use_timestamp', True)
else:
use_timestamp = True
finish_count = 0
file_count = 1
lfr_factor = 6
@ -582,13 +600,13 @@ def inference_modelscope(
if output_path is not None:
writer = DatadirWriter(output_path)
ibest_writer = writer[f"1best_recog"]
for keys, batch in loader:
assert isinstance(batch, dict), type(batch)
assert all(isinstance(s, str) for s in keys), keys
_bs = len(next(iter(batch.values())))
assert len(keys) == _bs, f"{len(keys)} != {_bs}"
vad_results = speech2vadsegment(**batch)
fbanks, vadsegments = vad_results[0], vad_results[1]
for i, segments in enumerate(vadsegments):
@ -602,18 +620,20 @@ def inference_modelscope(
results = speech2text(**batch)
if len(results) < 1:
continue
result_cur = [results[0][:-2]]
if j == 0:
result_segments = result_cur
else:
result_segments = [[result_segments[0][i] + result_cur[0][i] for i in range(len(result_cur[0]))]]
result_segments = [
[result_segments[0][i] + result_cur[0][i] for i in range(len(result_cur[0]))]]
key = keys[0]
result = result_segments[0]
text, token, token_int = result[0], result[1], result[2]
time_stamp = None if len(result) < 4 else result[3]
if use_timestamp and time_stamp is not None:
postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
else:
@ -631,13 +651,13 @@ def inference_modelscope(
text_postprocessed_punc = text_postprocessed
if len(word_lists) > 0 and text2punc is not None:
text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
item = {'key': key, 'value': text_postprocessed_punc}
if text_postprocessed != "":
item['text_postprocessed'] = text_postprocessed
if time_stamp_postprocessed != "":
item['time_stamp'] = time_stamp_postprocessed
asr_result_list.append(item)
finish_count += 1
# asr_utils.print_progress(finish_count / file_count)
@ -650,11 +670,13 @@ def inference_modelscope(
ibest_writer["text_with_punc"][key] = text_postprocessed_punc
if time_stamp_postprocessed is not None:
ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)
logging.info("decoding, utt: {}, predictions: {}".format(key, text_postprocessed_punc))
return asr_result_list
return _forward
def get_parser():
parser = config_argparse.ArgumentParser(
description="ASR Decoding",

15
funasr/bin/vad_inference.py
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@ -81,6 +81,7 @@ class Speech2VadSegment:
self.device = device
self.dtype = dtype
self.frontend = frontend
self.batch_size = batch_size
@torch.no_grad()
def __call__(
@ -106,13 +107,11 @@ class Speech2VadSegment:
feats_len = feats_len.int()
else:
raise Exception("Need to extract feats first, please configure frontend configuration")
# batch = {"feats": feats, "waveform": speech, "is_final_send": True}
# segments = self.vad_model(**batch)
# b. Forward Encoder sreaming
segments = []
step = 6000
# b. Forward Encoder streaming
t_offset = 0
step = min(feats_len, 6000)
segments = [[]] * self.batch_size
for t_offset in range(0, feats_len, min(step, feats_len - t_offset)):
if t_offset + step >= feats_len - 1:
step = feats_len - t_offset
@ -128,9 +127,8 @@ class Speech2VadSegment:
batch = to_device(batch, device=self.device)
segments_part = self.vad_model(**batch)
if segments_part:
segments += segments_part
#print(segments)
for batch_num in range(0, self.batch_size):
segments[batch_num] += segments_part[batch_num]
return segments
@ -254,7 +252,6 @@ def inference_modelscope(
assert all(isinstance(s, str) for s in keys), keys
_bs = len(next(iter(batch.values())))
assert len(keys) == _bs, f"{len(keys)} != {_bs}"
# batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}
# do vad segment
results = speech2vadsegment(**batch)

24
funasr/models/e2e_vad.py
View File

@ -192,7 +192,7 @@ class WindowDetector(object):
class E2EVadModel(nn.Module):
def __init__(self, encoder: FSMN, vad_post_args: Dict[str, Any], streaming=False):
def __init__(self, encoder: FSMN, vad_post_args: Dict[str, Any]):
super(E2EVadModel, self).__init__()
self.vad_opts = VADXOptions(**vad_post_args)
self.windows_detector = WindowDetector(self.vad_opts.window_size_ms,
@ -227,7 +227,6 @@ class E2EVadModel(nn.Module):
self.data_buf = None
self.data_buf_all = None
self.waveform = None
self.streaming = streaming
self.ResetDetection()
def AllResetDetection(self):
@ -451,11 +450,7 @@ class E2EVadModel(nn.Module):
if not is_final_send:
self.DetectCommonFrames()
else:
if self.streaming:
self.DetectLastFrames()
else:
self.AllResetDetection()
self.DetectAllFrames() # offline decode and is_final_send == True
self.DetectLastFrames()
segments = []
for batch_num in range(0, feats.shape[0]): # only support batch_size = 1 now
segment_batch = []
@ -468,7 +463,8 @@ class E2EVadModel(nn.Module):
self.output_data_buf_offset += 1 # need update this parameter
if segment_batch:
segments.append(segment_batch)
if is_final_send:
self.AllResetDetection()
return segments
def DetectCommonFrames(self) -> int:
@ -494,18 +490,6 @@ class E2EVadModel(nn.Module):
return 0
def DetectAllFrames(self) -> int:
if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
return 0
if self.vad_opts.nn_eval_block_size != self.vad_opts.dcd_block_size:
frame_state = FrameState.kFrameStateInvalid
for t in range(0, self.frm_cnt):
frame_state = self.GetFrameState(t)
self.DetectOneFrame(frame_state, t, t == self.frm_cnt - 1)
else:
pass
return 0
def DetectOneFrame(self, cur_frm_state: FrameState, cur_frm_idx: int, is_final_frame: bool) -> None:
tmp_cur_frm_state = FrameState.kFrameStateInvalid
if cur_frm_state == FrameState.kFrameStateSpeech:

3
funasr/tasks/vad.py
View File

@ -291,8 +291,7 @@ class VADTask(AbsTask):
model_class = model_choices.get_class(args.model)
except AttributeError:
model_class = model_choices.get_class("e2evad")
model = model_class(encoder=encoder, vad_post_args=args.vad_post_conf,
streaming=args.encoder_conf.get('streaming', False))
model = model_class(encoder=encoder, vad_post_args=args.vad_post_conf)
return model