From 28ccfbfc51068a663a80764e14074df5edf2b5ba Mon Sep 17 00:00:00 2001
From: kongdeqiang <kongdeqiang960204@163.com>
Date: 星期五, 13 三月 2026 17:41:41 +0800
Subject: [PATCH] 提交
---
funasr/models/sense_voice/model.py | 80 +++++++++++++++++++++++++--------------
1 files changed, 51 insertions(+), 29 deletions(-)
diff --git a/funasr/models/sense_voice/model.py b/funasr/models/sense_voice/model.py
index ca0c40a..6a29181 100644
--- a/funasr/models/sense_voice/model.py
+++ b/funasr/models/sense_voice/model.py
@@ -95,7 +95,7 @@
n_feat,
dropout_rate,
kernel_size,
- sanm_shift=0,
+ sanm_shfit=0,
lora_list=None,
lora_rank=8,
lora_alpha=16,
@@ -121,17 +121,17 @@
)
# padding
left_padding = (kernel_size - 1) // 2
- if sanm_shift > 0:
- left_padding = left_padding + sanm_shift
+ if sanm_shfit > 0:
+ left_padding = left_padding + sanm_shfit
right_padding = kernel_size - 1 - left_padding
self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0)
- def forward_fsmn(self, inputs, mask, mask_shift_chunk=None):
+ def forward_fsmn(self, inputs, mask, mask_shfit_chunk=None):
b, t, d = inputs.size()
if mask is not None:
mask = torch.reshape(mask, (b, -1, 1))
- if mask_shift_chunk is not None:
- mask = mask * mask_shift_chunk
+ if mask_shfit_chunk is not None:
+ mask = mask * mask_shfit_chunk
inputs = inputs * mask
x = inputs.transpose(1, 2)
@@ -211,7 +211,7 @@
return self.linear_out(x) # (batch, time1, d_model)
- def forward(self, x, mask, mask_shift_chunk=None, mask_att_chunk_encoder=None):
+ def forward(self, x, mask, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
"""Compute scaled dot product attention.
Args:
@@ -226,7 +226,7 @@
"""
q_h, k_h, v_h, v = self.forward_qkv(x)
- fsmn_memory = self.forward_fsmn(v, mask, mask_shift_chunk)
+ fsmn_memory = self.forward_fsmn(v, mask, mask_shfit_chunk)
q_h = q_h * self.d_k ** (-0.5)
scores = torch.matmul(q_h, k_h.transpose(-2, -1))
att_outs = self.forward_attention(v_h, scores, mask, mask_att_chunk_encoder)
@@ -326,7 +326,7 @@
self.stochastic_depth_rate = stochastic_depth_rate
self.dropout_rate = dropout_rate
- def forward(self, x, mask, cache=None, mask_shift_chunk=None, mask_att_chunk_encoder=None):
+ def forward(self, x, mask, cache=None, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
"""Compute encoded features.
Args:
@@ -363,7 +363,7 @@
self.self_attn(
x,
mask,
- mask_shift_chunk=mask_shift_chunk,
+ mask_shfit_chunk=mask_shfit_chunk,
mask_att_chunk_encoder=mask_att_chunk_encoder,
),
),
@@ -379,7 +379,7 @@
self.self_attn(
x,
mask,
- mask_shift_chunk=mask_shift_chunk,
+ mask_shfit_chunk=mask_shfit_chunk,
mask_att_chunk_encoder=mask_att_chunk_encoder,
)
)
@@ -388,7 +388,7 @@
self.self_attn(
x,
mask,
- mask_shift_chunk=mask_shift_chunk,
+ mask_shfit_chunk=mask_shfit_chunk,
mask_att_chunk_encoder=mask_att_chunk_encoder,
)
)
@@ -402,7 +402,7 @@
if not self.normalize_before:
x = self.norm2(x)
- return x, mask, cache, mask_shift_chunk, mask_att_chunk_encoder
+ return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder
def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0):
"""Compute encoded features.
@@ -469,7 +469,7 @@
positionwise_conv_kernel_size: int = 1,
padding_idx: int = -1,
kernel_size: int = 11,
- sanm_shift: int = 0,
+ sanm_shfit: int = 0,
selfattention_layer_type: str = "sanm",
**kwargs,
):
@@ -494,7 +494,7 @@
output_size,
attention_dropout_rate,
kernel_size,
- sanm_shift,
+ sanm_shfit,
)
encoder_selfattn_layer_args = (
attention_heads,
@@ -502,7 +502,7 @@
output_size,
attention_dropout_rate,
kernel_size,
- sanm_shift,
+ sanm_shfit,
)
self.encoders0 = nn.ModuleList(
@@ -919,17 +919,28 @@
timestamp = []
tokens = tokenizer.text2tokens(text)[4:]
+ token_back_to_id = tokenizer.tokens2ids(tokens)
+ token_ids = []
+ for tok_ls in token_back_to_id:
+ if tok_ls: token_ids.extend(tok_ls)
+ else: token_ids.append(124)
+
+ if len(token_ids) == 0:
+ result_i = {"key": key[i], "text": text}
+ results.append(result_i)
+ continue
+
logits_speech = self.ctc.softmax(encoder_out)[i, 4 : encoder_out_lens[i].item(), :]
pred = logits_speech.argmax(-1).cpu()
logits_speech[pred == self.blank_id, self.blank_id] = 0
align = ctc_forced_align(
logits_speech.unsqueeze(0).float(),
- torch.Tensor(token_int[4:]).unsqueeze(0).long().to(logits_speech.device),
- (encoder_out_lens - 4).long(),
- torch.tensor(len(token_int) - 4).unsqueeze(0).long().to(logits_speech.device),
+ torch.Tensor(token_ids).unsqueeze(0).long().to(logits_speech.device),
+ (encoder_out_lens[i] - 4).long(),
+ torch.tensor(len(token_ids)).unsqueeze(0).long().to(logits_speech.device),
ignore_id=self.ignore_id,
)
- pred = groupby(align[0, : encoder_out_lens[0]])
+ pred = groupby(align[0, : encoder_out_lens[i]])
_start = 0
token_id = 0
ts_max = encoder_out_lens[i] - 4
@@ -941,8 +952,8 @@
timestamp.append([tokens[token_id], ts_left, ts_right])
token_id += 1
_start = _end
- timestamp = self.post(timestamp)
- result_i = {"key": key[i], "text": text, "timestamp": timestamp}
+ timestamp, words = self.post(timestamp)
+ result_i = {"key": key[i], "text": text, "timestamp": timestamp, "words": words}
results.append(result_i)
else:
result_i = {"key": key[i], "text": text}
@@ -951,24 +962,35 @@
def post(self, timestamp):
timestamp_new = []
+ words_new = []
+ prev_word = None
for i, t in enumerate(timestamp):
word, start, end = t
+ start = int(start * 1000)
+ end = int(end * 1000)
if word == "鈻�":
continue
if i == 0:
# timestamp_new.append([word, start, end])
- timestamp_new.append([int(start * 1000), int(end * 1000)])
- elif word.startswith("鈻�") or len(word) == 1 or not word[1].isalpha():
+ timestamp_new.append([start, end])
+ words_new.append(word)
+ elif word.startswith("鈻�"):
word = word[1:]
- # timestamp_new.append([word, start, end])
- timestamp_new.append([int(start * 1000), int(end * 1000)])
+ timestamp_new.append([start, end])
+ words_new.append(word)
+ elif prev_word is not None and prev_word.isalpha() and prev_word.isascii() and word.isalpha() and word.isascii():
+ word = prev_word + word
+ timestamp_new[-1][1] = end
+ words_new[-1] = word
else:
# timestamp_new[-1][0] += word
- timestamp_new[-1][1] = int(end * 1000)
- return timestamp_new
+ timestamp_new.append([start, end])
+ words_new.append(word)
+ prev_word = word
+ return timestamp_new, words_new
def export(self, **kwargs):
- from export_meta import export_rebuild_model
+ from .export_meta import export_rebuild_model
if "max_seq_len" not in kwargs:
kwargs["max_seq_len"] = 512
--
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