speech_recognition/FunASR
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haoneng.lhn 2023-05-31 15:42:43 +08:00
parent 7ecf141574
commit 28cabff45f
4 changed files with 101 additions and 33 deletions
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37
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo.py
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@ -1,39 +1,12 @@
import os
import logging
import torch
import soundfile
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger
logger = get_logger(log_level=logging.CRITICAL)
logger.setLevel(logging.CRITICAL)
os.environ["MODELSCOPE_CACHE"] = "./"
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
model_revision='v1.0.4'
model_revision='v1.0.5',
mode="paraformer_fake_streaming"
)
model_dir = os.path.join(os.environ["MODELSCOPE_CACHE"], "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online")
speech, sample_rate = soundfile.read(os.path.join(model_dir, "example/asr_example.wav"))
speech_length = speech.shape[0]
sample_offset = 0
chunk_size = [5, 10, 5] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
stride_size = chunk_size[1] * 960
param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size}
final_result = ""
for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
if sample_offset + stride_size >= speech_length - 1:
stride_size = speech_length - sample_offset
param_dict["is_final"] = True
rec_result = inference_pipeline(audio_in=speech[sample_offset: sample_offset + stride_size],
param_dict=param_dict)
if len(rec_result) != 0:
final_result += rec_result['text']
print(rec_result)
print(final_result)
audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav'
rec_result = inference_pipeline(audio_in=audio_in)
print(rec_result)

40
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo_online.py Normal file
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@ -0,0 +1,40 @@
import os
import logging
import torch
import soundfile
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger
logger = get_logger(log_level=logging.CRITICAL)
logger.setLevel(logging.CRITICAL)
os.environ["MODELSCOPE_CACHE"] = "./"
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
model_revision='v1.0.4',
mode="paraformer_streaming"
)
model_dir = os.path.join(os.environ["MODELSCOPE_CACHE"], "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online")
speech, sample_rate = soundfile.read(os.path.join(model_dir, "example/asr_example.wav"))
speech_length = speech.shape[0]
sample_offset = 0
chunk_size = [5, 10, 5] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
stride_size = chunk_size[1] * 960
param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size}
final_result = ""
for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
if sample_offset + stride_size >= speech_length - 1:
stride_size = speech_length - sample_offset
param_dict["is_final"] = True
rec_result = inference_pipeline(audio_in=speech[sample_offset: sample_offset + stride_size],
param_dict=param_dict)
if len(rec_result) != 0:
final_result += rec_result['text']
print(rec_result)
print(final_result)

2
funasr/bin/asr_inference_launch.py
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@ -1618,7 +1618,7 @@ def inference_launch(**kwargs):
return inference_uniasr(**kwargs)
elif mode == "paraformer":
return inference_paraformer(**kwargs)
elif mode == "paraformer_online":
elif mode == "paraformer_fake_streaming":
return inference_paraformer(**kwargs)
elif mode == "paraformer_streaming":
return inference_paraformer_online(**kwargs)

55
funasr/models/encoder/sanm_encoder.py
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@ -633,6 +633,8 @@ class SANMEncoderChunkOpt(AbsEncoder):
self.embed = torch.nn.Linear(input_size, output_size)
elif input_layer == "pe":
self.embed = SinusoidalPositionEncoder()
elif input_layer == "pe_online":
self.embed = StreamSinusoidalPositionEncoder()
else:
raise ValueError("unknown input_layer: " + input_layer)
self.normalize_before = normalize_before
@ -818,6 +820,59 @@ class SANMEncoderChunkOpt(AbsEncoder):
return (xs_pad, intermediate_outs), olens, None
return xs_pad, olens, None
def _add_overlap_chunk(self, feats: np.ndarray, cache: dict = {}):
if len(cache) == 0:
return feats
cache["feats"] = to_device(cache["feats"], device=feats.device)
overlap_feats = torch.cat((cache["feats"], feats), dim=1)
cache["feats"] = overlap_feats[:, -(cache["chunk_size"][0] + cache["chunk_size"][2]):, :]
return overlap_feats
def forward_chunk(self,
xs_pad: torch.Tensor,
ilens: torch.Tensor,
cache: dict = None,
ctc: CTC = None,
):
xs_pad *= self.output_size() ** 0.5
if self.embed is None:
xs_pad = xs_pad
else:
xs_pad = self.embed(xs_pad, cache)
if cache["tail_chunk"]:
xs_pad = to_device(cache["feats"], device=xs_pad.device)
else:
xs_pad = self._add_overlap_chunk(xs_pad, cache)
encoder_outs = self.encoders0(xs_pad, None, None, None, None)
xs_pad, masks = encoder_outs[0], encoder_outs[1]
intermediate_outs = []
if len(self.interctc_layer_idx) == 0:
encoder_outs = self.encoders(xs_pad, None, None, None, None)
xs_pad, masks = encoder_outs[0], encoder_outs[1]
else:
for layer_idx, encoder_layer in enumerate(self.encoders):
encoder_outs = encoder_layer(xs_pad, None, None, None, None)
xs_pad, masks = encoder_outs[0], encoder_outs[1]
if layer_idx + 1 in self.interctc_layer_idx:
encoder_out = xs_pad
# intermediate outputs are also normalized
if self.normalize_before:
encoder_out = self.after_norm(encoder_out)
intermediate_outs.append((layer_idx + 1, encoder_out))
if self.interctc_use_conditioning:
ctc_out = ctc.softmax(encoder_out)
xs_pad = xs_pad + self.conditioning_layer(ctc_out)
if self.normalize_before:
xs_pad = self.after_norm(xs_pad)
if len(intermediate_outs) > 0:
return (xs_pad, intermediate_outs), None, None
return xs_pad, ilens, None
def gen_tf2torch_map_dict(self):
tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf