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fix chunked infer #10581

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merged 2 commits into from
Oct 8, 2024
Merged

fix chunked infer #10581

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e8f9fb4
fix chunked infer
stevehuang52 Sep 23, 2024
85af988
Merge branch 'main' into fix_chunked_infer
stevehuang52 Sep 23, 2024
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6 changes: 1 addition & 5 deletions examples/asr/asr_chunked_inference/aed/speech_to_text_aed_chunked_infer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -119,11 +119,7 @@ def main(cfg: TranscriptionConfig) -> TranscriptionConfig:
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
torch.set_grad_enabled(False)

for key in cfg:
cfg[key] = None if cfg[key] == 'None' else cfg[key]

if is_dataclass(cfg):
cfg = OmegaConf.structured(cfg)
cfg = OmegaConf.structured(cfg)

if cfg.random_seed:
pl.seed_everything(cfg.random_seed)
Expand Down
25 changes: 8 additions & 17 deletions examples/asr/asr_chunked_inference/ctc/speech_to_text_buffered_infer_ctc.py
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Expand Up @@ -88,7 +88,9 @@ class TranscriptionConfig:
# Chunked configs
chunk_len_in_secs: float = 1.6 # Chunk length in seconds
total_buffer_in_secs: float = 4.0 # Length of buffer (chunk + left and right padding) in seconds
model_stride: int = 8 # Model downsampling factor, 8 for Citrinet and FasConformer models and 4 for Conformer models.
model_stride: int = (
8 # Model downsampling factor, 8 for Citrinet and FasConformer models and 4 for Conformer models.
)

# Decoding strategy for CTC models
decoding: CTCDecodingConfig = CTCDecodingConfig()
Expand All @@ -115,11 +117,7 @@ def main(cfg: TranscriptionConfig) -> TranscriptionConfig:
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
torch.set_grad_enabled(False)

for key in cfg:
cfg[key] = None if cfg[key] == 'None' else cfg[key]

if is_dataclass(cfg):
cfg = OmegaConf.structured(cfg)
cfg = OmegaConf.structured(cfg)

if cfg.random_seed:
pl.seed_everything(cfg.random_seed)
Expand Down Expand Up @@ -163,16 +161,6 @@ def main(cfg: TranscriptionConfig) -> TranscriptionConfig:
# Disable config overwriting
OmegaConf.set_struct(model_cfg.preprocessor, True)

# setup AMP (optional)
if cfg.amp and torch.cuda.is_available() and hasattr(torch.cuda, 'amp') and hasattr(torch.cuda.amp, 'autocast'):
logging.info("AMP enabled!\n")
autocast = torch.cuda.amp.autocast
else:

@contextlib.contextmanager
def autocast():
yield

# Compute output filename
cfg = compute_output_filename(cfg, model_name)

Expand Down Expand Up @@ -214,7 +202,10 @@ def autocast():
logging.info(f"tokens_per_chunk is {tokens_per_chunk}, mid_delay is {mid_delay}")

frame_asr = FrameBatchASR(
asr_model=asr_model, frame_len=chunk_len, total_buffer=cfg.total_buffer_in_secs, batch_size=cfg.batch_size,
asr_model=asr_model,
frame_len=chunk_len,
total_buffer=cfg.total_buffer_in_secs,
batch_size=cfg.batch_size,
)

hyps = get_buffered_pred_feat(
Expand Down
10 changes: 4 additions & 6 deletions examples/asr/asr_chunked_inference/rnnt/speech_to_text_buffered_infer_rnnt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,7 +112,9 @@ class TranscriptionConfig:
# Chunked configs
chunk_len_in_secs: float = 1.6 # Chunk length in seconds
total_buffer_in_secs: float = 4.0 # Length of buffer (chunk + left and right padding) in seconds
model_stride: int = 8 # Model downsampling factor, 8 for Citrinet and FastConformer models and 4 for Conformer models.
model_stride: int = (
8 # Model downsampling factor, 8 for Citrinet and FastConformer models and 4 for Conformer models.
)

# Set `cuda` to int to define CUDA device. If 'None', will look for CUDA
# device anyway, and do inference on CPU only if CUDA device is not found.
Expand Down Expand Up @@ -146,11 +148,7 @@ def main(cfg: TranscriptionConfig) -> TranscriptionConfig:
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
torch.set_grad_enabled(False)

for key in cfg:
cfg[key] = None if cfg[key] == 'None' else cfg[key]

if is_dataclass(cfg):
cfg = OmegaConf.structured(cfg)
cfg = OmegaConf.structured(cfg)

if cfg.random_seed:
pl.seed_everything(cfg.random_seed)
Expand Down
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