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Improve RNN-T streaming decoding #3295

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d5e1210
Improve RNN-T streaming decoding
lakshmi-speak May 2, 2023
768307f
Merge branch 'main' into rnnt_decoder_fix_streaming
lakshmi-speak May 24, 2023
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6 changes: 3 additions & 3 deletions examples/asr/emformer_rnnt/pipeline_demo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,9 +65,9 @@ def run_eval_streaming(args):
with torch.no_grad():
features, length = streaming_feature_extractor(segment)
hypos, state = decoder.infer(features, length, 10, state=state, hypothesis=hypothesis)
hypothesis = hypos[0]
transcript = token_processor(hypothesis[0], lstrip=False)
print(transcript, end="", flush=True)
hypothesis = hypos
transcript = token_processor(hypos[0][0], lstrip=True)
print(transcript, end="\r", flush=True)
print()

# Non-streaming decode.
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7 changes: 4 additions & 3 deletions examples/tutorials/online_asr_tutorial.py
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Expand Up @@ -39,6 +39,7 @@
# --------------
#

import os
import torch
import torchaudio

Expand Down Expand Up @@ -222,9 +223,9 @@ def run_inference(num_iter=100):
segment = cacher(chunk[:, 0])
features, length = feature_extractor(segment)
hypos, state = decoder.infer(features, length, 10, state=state, hypothesis=hypothesis)
hypothesis = hypos[0]
transcript = token_processor(hypothesis[0], lstrip=False)
print(transcript, end="", flush=True)
hypothesis = hypos
transcript = token_processor(hypos[0][0], lstrip=False)
print(transcript, end="\r", flush=True)

chunks.append(chunk)
feats.append(features)
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4 changes: 2 additions & 2 deletions test/torchaudio_unittest/models/rnnt_decoder/rnnt_decoder_test_impl.py
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Expand Up @@ -99,7 +99,7 @@ def test_torchscript_consistency_infer(self):
self.assertEqual(res, scripted_res)

state = res[1]
hypo = res[0][0]
hypo = res[0]

scripted_state = scripted_res[1]
scripted_hypo = scripted_res[0][0]
scripted_hypo = scripted_res[0]
22 changes: 9 additions & 13 deletions torchaudio/models/rnnt_decoder.py
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Expand Up @@ -109,13 +109,9 @@ def __init__(

self.step_max_tokens = step_max_tokens

def _init_b_hypos(self, hypo: Optional[Hypothesis], device: torch.device) -> List[Hypothesis]:
if hypo is not None:
token = _get_hypo_tokens(hypo)[-1]
state = _get_hypo_state(hypo)
else:
token = self.blank
state = None
def _init_b_hypos(self, device: torch.device) -> List[Hypothesis]:
token = self.blank
state = None

one_tensor = torch.tensor([1], device=device)
pred_out, _, pred_state = self.model.predict(torch.tensor([[token]], device=device), one_tensor, state)
Expand Down Expand Up @@ -230,14 +226,14 @@ def _gen_new_hypos(
def _search(
self,
enc_out: torch.Tensor,
hypo: Optional[Hypothesis],
hypo: Optional[List[Hypothesis]],
beam_width: int,
) -> List[Hypothesis]:
n_time_steps = enc_out.shape[1]
device = enc_out.device

a_hypos: List[Hypothesis] = []
b_hypos = self._init_b_hypos(hypo, device)
b_hypos = self._init_b_hypos(device) if hypo is None else hypo
for t in range(n_time_steps):
a_hypos = b_hypos
b_hypos = torch.jit.annotate(List[Hypothesis], [])
Expand All @@ -263,7 +259,7 @@ def _search(
if a_hypos:
symbols_current_t += 1

_, sorted_idx = torch.tensor([self.hypo_sort_key(hypo) for hypo in b_hypos]).topk(beam_width)
_, sorted_idx = torch.tensor([self.hypo_sort_key(hyp) for hyp in b_hypos]).topk(beam_width)
b_hypos = [b_hypos[idx] for idx in sorted_idx]

return b_hypos
Expand All @@ -290,8 +286,8 @@ def forward(self, input: torch.Tensor, length: torch.Tensor, beam_width: int) ->

if length.shape != () and length.shape != (1,):
raise ValueError("length must be of shape () or (1,)")
if input.dim() == 0:
input = input.unsqueeze(0)
if length.dim() == 0:
length = length.unsqueeze(0)

enc_out, _ = self.model.transcribe(input, length)
return self._search(enc_out, None, beam_width)
Expand All @@ -303,7 +299,7 @@ def infer(
length: torch.Tensor,
beam_width: int,
state: Optional[List[List[torch.Tensor]]] = None,
hypothesis: Optional[Hypothesis] = None,
hypothesis: Optional[List[Hypothesis]] = None,
) -> Tuple[List[Hypothesis], List[List[torch.Tensor]]]:
r"""Performs beam search for the given input sequence in streaming mode.

Expand Down