speech_recognition/FunASR
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support offline inference for unified streaming/non-streaming rnnt

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This commit is contained in:
aky15 2023-08-14 10:54:23 +08:00
parent 51fdffc1a0
commit a5cd4bb473
2 changed files with 39 additions and 1 deletions
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34
funasr/bin/asr_infer.py
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@ -1336,6 +1336,7 @@ class Speech2TextTransducer:
nbest: int = 1,
streaming: bool = False,
simu_streaming: bool = False,
full_utt: bool = False,
chunk_size: int = 16,
left_context: int = 32,
right_context: int = 0,
@ -1430,6 +1431,7 @@ class Speech2TextTransducer:
self.beam_search = beam_search
self.streaming = streaming
self.simu_streaming = simu_streaming
self.full_utt = full_utt
self.chunk_size = max(chunk_size, 0)
self.left_context = left_context
self.right_context = max(right_context, 0)
@ -1449,6 +1451,7 @@ class Speech2TextTransducer:
self._ctx = self.asr_model.encoder.get_encoder_input_size(
self.window_size
)
self._right_ctx = right_context
self.last_chunk_length = (
self.asr_model.encoder.embed.min_frame_length + self.right_context + 1
@ -1545,6 +1548,37 @@ class Speech2TextTransducer:
return nbest_hyps
@torch.no_grad()
def full_utt_decode(self, speech: Union[torch.Tensor, np.ndarray]) -> List[HypothesisTransducer]:
"""Speech2Text call.
Args:
speech: Speech data. (S)
Returns:
nbest_hypothesis: N-best hypothesis.
"""
assert check_argument_types()
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
if self.frontend is not None:
speech = torch.unsqueeze(speech, axis=0)
speech_lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1))
feats, feats_lengths = self.frontend(speech, speech_lengths)
else:
feats = speech.unsqueeze(0).to(getattr(torch, self.dtype))
feats_lengths = feats.new_full([1], dtype=torch.long, fill_value=feats.size(1))
if self.asr_model.normalize is not None:
feats, feats_lengths = self.asr_model.normalize(feats, feats_lengths)
feats = to_device(feats, device=self.device)
feats_lengths = to_device(feats_lengths, device=self.device)
enc_out = self.asr_model.encoder.full_utt_forward(feats, feats_lengths)
nbest_hyps = self.beam_search(enc_out[0])
return nbest_hyps
@torch.no_grad()
def __call__(self, speech: Union[torch.Tensor, np.ndarray]) -> List[HypothesisTransducer]:
"""Speech2Text call.

6
funasr/bin/asr_inference_launch.py
View File

@ -1290,6 +1290,7 @@ def inference_transducer(
quantize_dtype: Optional[str] = "float16",
streaming: Optional[bool] = False,
simu_streaming: Optional[bool] = False,
full_utt: Optional[bool] = False,
chunk_size: Optional[int] = 16,
left_context: Optional[int] = 16,
right_context: Optional[int] = 0,
@ -1366,6 +1367,7 @@ def inference_transducer(
quantize_dtype=quantize_dtype,
streaming=streaming,
simu_streaming=simu_streaming,
full_utt=full_utt,
chunk_size=chunk_size,
left_context=left_context,
right_context=right_context,
@ -1416,7 +1418,7 @@ def inference_transducer(
_end = (i + 1) * speech2text._ctx
speech2text.streaming_decode(
speech[i * speech2text._ctx: _end], is_final=False
speech[i * speech2text._ctx: _end + speech2text._right_ctx], is_final=False
)
final_hyps = speech2text.streaming_decode(
@ -1424,6 +1426,8 @@ def inference_transducer(
)
elif speech2text.simu_streaming:
final_hyps = speech2text.simu_streaming_decode(**batch)
elif speech2text.full_utt:
final_hyps = speech2text.full_utt_decode(**batch)
else:
final_hyps = speech2text(**batch)