From d2f1cf39f8fedc19d0e14fac269a413d62375359 Mon Sep 17 00:00:00 2001
From: 语帆 <yf352572@alibaba-inc.com>
Date: 星期三, 21 二月 2024 17:03:29 +0800
Subject: [PATCH] test
---
funasr/models/contextual_paraformer/model.py | 47 +++++++++++++++++++++++++++++++++--------------
1 files changed, 33 insertions(+), 14 deletions(-)
diff --git a/funasr/models/contextual_paraformer/model.py b/funasr/models/contextual_paraformer/model.py
index 939d46d..5ccc611 100644
--- a/funasr/models/contextual_paraformer/model.py
+++ b/funasr/models/contextual_paraformer/model.py
@@ -29,7 +29,7 @@
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
-
+import pdb
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
from torch.cuda.amp import autocast
@@ -62,7 +62,6 @@
crit_attn_weight = kwargs.get("crit_attn_weight", 0.0)
crit_attn_smooth = kwargs.get("crit_attn_smooth", 0.0)
bias_encoder_dropout_rate = kwargs.get("bias_encoder_dropout_rate", 0.0)
-
if bias_encoder_type == 'lstm':
self.bias_encoder = torch.nn.LSTM(inner_dim, inner_dim, 1, batch_first=True, dropout=bias_encoder_dropout_rate)
@@ -103,17 +102,17 @@
text_lengths = text_lengths[:, 0]
if len(speech_lengths.size()) > 1:
speech_lengths = speech_lengths[:, 0]
-
+ pdb.set_trace()
batch_size = speech.shape[0]
hotword_pad = kwargs.get("hotword_pad")
hotword_lengths = kwargs.get("hotword_lengths")
dha_pad = kwargs.get("dha_pad")
-
+ pdb.set_trace()
# 1. Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
-
+ pdb.set_trace()
loss_ctc, cer_ctc = None, None
stats = dict()
@@ -128,12 +127,12 @@
stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
stats["cer_ctc"] = cer_ctc
-
+ pdb.set_trace()
# 2b. Attention decoder branch
loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal = self._calc_att_clas_loss(
encoder_out, encoder_out_lens, text, text_lengths, hotword_pad, hotword_lengths
)
-
+ pdb.set_trace()
# 3. CTC-Att loss definition
if self.ctc_weight == 0.0:
loss = loss_att + loss_pre * self.predictor_weight
@@ -171,22 +170,26 @@
):
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
encoder_out.device)
+ pdb.set_trace()
if self.predictor_bias == 1:
_, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
ys_pad_lens = ys_pad_lens + self.predictor_bias
+ pdb.set_trace()
pre_acoustic_embeds, pre_token_length, _, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask,
ignore_id=self.ignore_id)
-
+ pdb.set_trace()
# -1. bias encoder
if self.use_decoder_embedding:
hw_embed = self.decoder.embed(hotword_pad)
else:
hw_embed = self.bias_embed(hotword_pad)
+ pdb.set_trace()
hw_embed, (_, _) = self.bias_encoder(hw_embed)
+ pdb.set_trace()
_ind = np.arange(0, hotword_pad.shape[0]).tolist()
selected = hw_embed[_ind, [i - 1 for i in hotword_lengths.detach().cpu().tolist()]]
contextual_info = selected.squeeze(0).repeat(ys_pad.shape[0], 1, 1).to(ys_pad.device)
-
+ pdb.set_trace()
# 0. sampler
decoder_out_1st = None
if self.sampling_ratio > 0.0:
@@ -198,7 +201,7 @@
if self.step_cur < 2:
logging.info("disable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
sematic_embeds = pre_acoustic_embeds
-
+ pdb.set_trace()
# 1. Forward decoder
decoder_outs = self.decoder(
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=contextual_info
@@ -214,7 +217,7 @@
loss_ideal = None
'''
loss_ideal = None
-
+ pdb.set_trace()
if decoder_out_1st is None:
decoder_out_1st = decoder_out
# 2. Compute attention loss
@@ -308,20 +311,24 @@
**kwargs,
):
# init beamsearch
+ pdb.set_trace()
is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
if self.beam_search is None and (is_use_lm or is_use_ctc):
logging.info("enable beam_search")
self.init_beam_search(**kwargs)
self.nbest = kwargs.get("nbest", 1)
-
+ pdb.set_trace()
meta_data = {}
# extract fbank feats
time1 = time.perf_counter()
+ pdb.set_trace()
audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
+ pdb.set_trace()
time2 = time.perf_counter()
meta_data["load_data"] = f"{time2 - time1:0.3f}"
+ pdb.set_trace()
speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
frontend=frontend)
time3 = time.perf_counter()
@@ -329,38 +336,50 @@
meta_data[
"batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
+ pdb.set_trace()
speech = speech.to(device=kwargs["device"])
speech_lengths = speech_lengths.to(device=kwargs["device"])
# hotword
+ pdb.set_trace()
self.hotword_list = self.generate_hotwords_list(kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend)
+ pdb.set_trace()
+
# Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
if isinstance(encoder_out, tuple):
encoder_out = encoder_out[0]
-
+ pdb.set_trace()
+
+
# predictor
predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
predictor_outs[2], predictor_outs[3]
+ pdb.set_trace()
pre_token_length = pre_token_length.round().long()
if torch.max(pre_token_length) < 1:
return []
-
+ pdb.set_trace()
+
decoder_outs = self.cal_decoder_with_predictor(encoder_out, encoder_out_lens,
pre_acoustic_embeds,
pre_token_length,
hw_list=self.hotword_list,
clas_scale=kwargs.get("clas_scale", 1.0))
+ pdb.set_trace()
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
+ pdb.set_trace()
results = []
b, n, d = decoder_out.size()
+ pdb.set_trace()
for i in range(b):
x = encoder_out[i, :encoder_out_lens[i], :]
am_scores = decoder_out[i, :pre_token_length[i], :]
+ pdb.set_trace()
if self.beam_search is not None:
nbest_hyps = self.beam_search(
x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0),
--
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