From 33d3d2084403fd34b79c835d2f2fe04f6cd8f738 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 13 九月 2023 09:33:54 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
funasr/bin/asr_infer.py | 40 ++++++++++++++++++++++++++++++++++++++--
1 files changed, 38 insertions(+), 2 deletions(-)
diff --git a/funasr/bin/asr_infer.py b/funasr/bin/asr_infer.py
index 02ca63d..7746821 100644
--- a/funasr/bin/asr_infer.py
+++ b/funasr/bin/asr_infer.py
@@ -399,7 +399,7 @@
@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,
+ decoding_ind: int = None, begin_time: int = 0, end_time: int = None,
):
"""Inference
@@ -429,7 +429,9 @@
batch = to_device(batch, device=self.device)
# b. Forward Encoder
- enc, enc_len = self.asr_model.encode(**batch, ind=self.decoding_ind)
+ if decoding_ind is None:
+ decoding_ind = self.decoding_ind
+ enc, enc_len = self.asr_model.encode(**batch, ind=decoding_ind)
if isinstance(enc, tuple):
enc = enc[0]
# assert len(enc) == 1, len(enc)
@@ -1336,6 +1338,7 @@
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 +1433,7 @@
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 +1453,7 @@
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
@@ -1546,6 +1551,37 @@
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.
Args:
--
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