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fixed formatting issues

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This commit is contained in:
jinzr 2023-09-24 17:02:01 +08:00
parent 78b2279969
commit 39cf318ba8
8 changed files with 447 additions and 400 deletions
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4
egs/librispeech/ASR/streaming_conformer_ctc/conformer.py
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@ -230,7 +230,9 @@ class Conformer(Transformer):
x, pos_emb, mask=mask, src_key_padding_mask=src_key_padding_mask
) # (T, B, F)
else:
x = self.encoder(x, pos_emb, src_key_padding_mask=src_key_padding_mask) # (T, B, F)
x = self.encoder(
x, pos_emb, src_key_padding_mask=src_key_padding_mask
) # (T, B, F)
if self.normalize_before:
x = self.after_norm(x)

30
egs/librispeech/ASR/zipformer/decoder.py
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@ -61,10 +61,15 @@ class Decoder(nn.Module):
)
# the balancers are to avoid any drift in the magnitude of the
# embeddings, which would interact badly with parameter averaging.
self.balancer = Balancer(decoder_dim, channel_dim=-1,
min_positive=0.0, max_positive=1.0,
min_abs=0.5, max_abs=1.0,
prob=0.05)
self.balancer = Balancer(
decoder_dim,
channel_dim=-1,
min_positive=0.0,
max_positive=1.0,
min_abs=0.5,
max_abs=1.0,
prob=0.05,
)
self.blank_id = blank_id
@ -81,10 +86,15 @@ class Decoder(nn.Module):
groups=decoder_dim // 4, # group size == 4
bias=False,
)
self.balancer2 = Balancer(decoder_dim, channel_dim=-1,
min_positive=0.0, max_positive=1.0,
min_abs=0.5, max_abs=1.0,
prob=0.05)
self.balancer2 = Balancer(
decoder_dim,
channel_dim=-1,
min_positive=0.0,
max_positive=1.0,
min_abs=0.5,
max_abs=1.0,
prob=0.05,
)
def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor:
"""
@ -107,9 +117,7 @@ class Decoder(nn.Module):
if self.context_size > 1:
embedding_out = embedding_out.permute(0, 2, 1)
if need_pad is True:
embedding_out = F.pad(
embedding_out, pad=(self.context_size - 1, 0)
)
embedding_out = F.pad(embedding_out, pad=(self.context_size - 1, 0))
else:
# During inference time, there is no need to do extra padding
# as we only need one output

9
egs/librispeech/ASR/zipformer/joiner.py
View File

@ -52,12 +52,13 @@ class Joiner(nn.Module):
Returns:
Return a tensor of shape (N, T, s_range, C).
"""
assert encoder_out.ndim == decoder_out.ndim, (encoder_out.shape, decoder_out.shape)
assert encoder_out.ndim == decoder_out.ndim, (
encoder_out.shape,
decoder_out.shape,
)
if project_input:
logit = self.encoder_proj(encoder_out) + self.decoder_proj(
decoder_out
)
logit = self.encoder_proj(encoder_out) + self.decoder_proj(decoder_out)
else:
logit = encoder_out + decoder_out

4
egs/librispeech/ASR/zipformer/onnx_decode.py
View File

@ -303,7 +303,9 @@ def main():
for test_set, test_dl in zip(test_sets, test_dl):
start_time = time.time()
results, total_duration = decode_dataset(dl=test_dl, model=model, token_table=token_table)
results, total_duration = decode_dataset(
dl=test_dl, model=model, token_table=token_table
)
end_time = time.time()
elapsed_seconds = end_time - start_time
rtf = elapsed_seconds / total_duration

12
egs/librispeech/ASR/zipformer/profile.py
View File

@ -100,17 +100,13 @@ class Model(nn.Module):
self.encoder_embed = encoder_embed
self.encoder_proj = encoder_proj
def forward(
self, feature: Tensor, feature_lens: Tensor
) -> Tuple[Tensor, Tensor]:
def forward(self, feature: Tensor, feature_lens: Tensor) -> Tuple[Tensor, Tensor]:
x, x_lens = self.encoder_embed(feature, feature_lens)
src_key_padding_mask = make_pad_mask(x_lens)
x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C)
encoder_out, encoder_out_lens = self.encoder(
x, x_lens, src_key_padding_mask
)
encoder_out, encoder_out_lens = self.encoder(x, x_lens, src_key_padding_mask)
encoder_out = encoder_out.permute(1, 0, 2) # (N, T, C) -> (T, N, C)
logits = self.encoder_proj(encoder_out)
@ -168,9 +164,7 @@ def main():
if __name__ == "__main__":
formatter = (
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
)
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)
main()

483
egs/librispeech/ASR/zipformer/scaling.py
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File diff suppressed because it is too large Load Diff

57
egs/librispeech/ASR/zipformer/streaming_decode.py
View File

@ -282,9 +282,7 @@ def stack_states(state_list: List[List[torch.Tensor]]) -> List[torch.Tensor]:
)
batch_states.append(cached_embed_left_pad)
processed_lens = torch.cat(
[state_list[i][-1] for i in range(batch_size)], dim=0
)
processed_lens = torch.cat([state_list[i][-1] for i in range(batch_size)], dim=0)
batch_states.append(processed_lens)
return batch_states
@ -322,9 +320,7 @@ def unstack_states(batch_states: List[Tensor]) -> List[List[Tensor]]:
for layer in range(tot_num_layers):
layer_offset = layer * 6
# cached_key: (left_context_len, batch_size, key_dim)
cached_key_list = batch_states[layer_offset].chunk(
chunks=batch_size, dim=1
)
cached_key_list = batch_states[layer_offset].chunk(chunks=batch_size, dim=1)
# cached_nonlin_attn: (num_heads, batch_size, left_context_len, head_dim)
cached_nonlin_attn_list = batch_states[layer_offset + 1].chunk(
chunks=batch_size, dim=1
@ -355,9 +351,7 @@ def unstack_states(batch_states: List[Tensor]) -> List[List[Tensor]]:
cached_conv2_list[i],
]
cached_embed_left_pad_list = batch_states[-2].chunk(
chunks=batch_size, dim=0
)
cached_embed_left_pad_list = batch_states[-2].chunk(chunks=batch_size, dim=0)
for i in range(batch_size):
state_list[i].append(cached_embed_left_pad_list[i])
@ -380,11 +374,7 @@ def streaming_forward(
Returns encoder outputs, output lengths, and updated states.
"""
cached_embed_left_pad = states[-2]
(
x,
x_lens,
new_cached_embed_left_pad,
) = model.encoder_embed.streaming_forward(
(x, x_lens, new_cached_embed_left_pad,) = model.encoder_embed.streaming_forward(
x=features,
x_lens=feature_lens,
cached_left_pad=cached_embed_left_pad,
@ -404,9 +394,7 @@ def streaming_forward(
new_processed_lens = processed_lens + x_lens
# (batch, left_context_size + chunk_size)
src_key_padding_mask = torch.cat(
[processed_mask, src_key_padding_mask], dim=1
)
src_key_padding_mask = torch.cat([processed_mask, src_key_padding_mask], dim=1)
x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C)
encoder_states = states[:-2]
@ -494,9 +482,7 @@ def decode_one_chunk(
encoder_out = model.joiner.encoder_proj(encoder_out)
if params.decoding_method == "greedy_search":
greedy_search(
model=model, encoder_out=encoder_out, streams=decode_streams
)
greedy_search(model=model, encoder_out=encoder_out, streams=decode_streams)
elif params.decoding_method == "fast_beam_search":
processed_lens = torch.tensor(processed_lens, device=device)
processed_lens = processed_lens + encoder_out_lens
@ -517,9 +503,7 @@ def decode_one_chunk(
num_active_paths=params.num_active_paths,
)
else:
raise ValueError(
f"Unsupported decoding method: {params.decoding_method}"
)
raise ValueError(f"Unsupported decoding method: {params.decoding_method}")
states = unstack_states(new_states)
@ -577,9 +561,7 @@ def decode_dataset(
decode_streams = []
for num, cut in enumerate(cuts):
# each utterance has a DecodeStream.
initial_states = get_init_states(
model=model, batch_size=1, device=device
)
initial_states = get_init_states(model=model, batch_size=1, device=device)
decode_stream = DecodeStream(
params=params,
cut_id=cut.id,
@ -649,9 +631,7 @@ def decode_dataset(
elif params.decoding_method == "modified_beam_search":
key = f"num_active_paths_{params.num_active_paths}"
else:
raise ValueError(
f"Unsupported decoding method: {params.decoding_method}"
)
raise ValueError(f"Unsupported decoding method: {params.decoding_method}")
return {key: decode_results}
@ -684,8 +664,7 @@ def save_results(
test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
errs_info = (
params.res_dir
/ f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
)
with open(errs_info, "w") as f:
print("settings\tWER", file=f)
@ -718,9 +697,7 @@ def main():
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
assert params.causal, params.causal
assert (
"," not in params.chunk_size
), "chunk_size should be one value in decoding."
assert "," not in params.chunk_size, "chunk_size should be one value in decoding."
assert (
"," not in params.left_context_frames
), "left_context_frames should be one value in decoding."
@ -760,9 +737,9 @@ def main():
if not params.use_averaged_model:
if params.iter > 0:
filenames = find_checkpoints(
params.exp_dir, iteration=-params.iter
)[: params.avg]
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
: params.avg
]
if len(filenames) == 0:
raise ValueError(
f"No checkpoints found for"
@ -789,9 +766,9 @@ def main():
model.load_state_dict(average_checkpoints(filenames, device=device))
else:
if params.iter > 0:
filenames = find_checkpoints(
params.exp_dir, iteration=-params.iter
)[: params.avg + 1]
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
: params.avg + 1
]
if len(filenames) == 0:
raise ValueError(
f"No checkpoints found for"

14
egs/librispeech/ASR/zipformer/subsampling.py
View File

@ -107,9 +107,7 @@ class ConvNeXt(nn.Module):
if layerdrop_rate != 0.0:
batch_size = x.shape[0]
mask = (
torch.rand(
(batch_size, 1, 1, 1), dtype=x.dtype, device=x.device
)
torch.rand((batch_size, 1, 1, 1), dtype=x.dtype, device=x.device)
> layerdrop_rate
)
else:
@ -278,9 +276,7 @@ class Conv2dSubsampling(nn.Module):
# many copies of this extra gradient term.
self.out_whiten = Whiten(
num_groups=1,
whitening_limit=ScheduledFloat(
(0.0, 4.0), (20000.0, 8.0), default=4.0
),
whitening_limit=ScheduledFloat((0.0, 4.0), (20000.0, 8.0), default=4.0),
prob=(0.025, 0.25),
grad_scale=0.02,
)
@ -403,8 +399,8 @@ class Conv2dSubsampling(nn.Module):
left_pad = self.convnext.padding[0]
freq = self.out_width
channels = self.layer3_channels
cached_embed_left_pad = torch.zeros(
batch_size, channels, left_pad, freq
).to(device)
cached_embed_left_pad = torch.zeros(batch_size, channels, left_pad, freq).to(
device
)
return cached_embed_left_pad