From 62178770dccdbf5da42e831898ea32adeeacba45 Mon Sep 17 00:00:00 2001
From: 语帆 <yf352572@alibaba-inc.com>
Date: 星期三, 21 二月 2024 20:04:01 +0800
Subject: [PATCH] test
---
funasr/models/seaco_paraformer/model.py | 66 +++++++++++++++++++++++++--------
1 files changed, 50 insertions(+), 16 deletions(-)
diff --git a/funasr/models/seaco_paraformer/model.py b/funasr/models/seaco_paraformer/model.py
index 1867bbf..b3b9133 100644
--- a/funasr/models/seaco_paraformer/model.py
+++ b/funasr/models/seaco_paraformer/model.py
@@ -32,7 +32,7 @@
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
else:
@@ -66,7 +66,6 @@
# bias encoder
if self.bias_encoder_type == 'lstm':
- logging.warning("enable bias encoder sampling and contextual training")
self.bias_encoder = torch.nn.LSTM(self.inner_dim,
self.inner_dim,
2,
@@ -79,7 +78,6 @@
self.lstm_proj = None
self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
elif self.bias_encoder_type == 'mean':
- logging.warning("enable bias encoder sampling and contextual training")
self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
else:
logging.error("Unsupport bias encoder type: {}".format(self.bias_encoder_type))
@@ -132,7 +130,7 @@
hotword_pad = kwargs.get("hotword_pad")
hotword_lengths = kwargs.get("hotword_lengths")
dha_pad = kwargs.get("dha_pad")
-
+
batch_size = speech.shape[0]
self.step_cur += 1
# for data-parallel
@@ -214,58 +212,87 @@
nfilter=50,
seaco_weight=1.0):
# decoder forward
+ pdb.set_trace()
decoder_out, decoder_hidden, _ = self.decoder(encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, return_hidden=True, return_both=True)
+ pdb.set_trace()
decoder_pred = torch.log_softmax(decoder_out, dim=-1)
if hw_list is not None:
+ pdb.set_trace()
hw_lengths = [len(i) for i in hw_list]
hw_list_ = [torch.Tensor(i).long() for i in hw_list]
hw_list_pad = pad_list(hw_list_, 0).to(encoder_out.device)
+ pdb.set_trace()
selected = self._hotword_representation(hw_list_pad, torch.Tensor(hw_lengths).int().to(encoder_out.device))
+ pdb.set_trace()
contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
+ pdb.set_trace()
num_hot_word = contextual_info.shape[1]
_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
-
+ pdb.set_trace()
# ASF Core
if nfilter > 0 and nfilter < num_hot_word:
for dec in self.seaco_decoder.decoders:
dec.reserve_attn = True
+ pdb.set_trace()
# cif_attended, _ = self.decoder2(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
# cif_filter = torch.topk(self.decoder2.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1], min(nfilter, num_hot_word-1))[1].tolist()
+ pdb.set_trace()
hotword_scores = self.seaco_decoder.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1]
# hotword_scores /= torch.sqrt(torch.tensor(hw_lengths)[:-1].float()).to(hotword_scores.device)
+ pdb.set_trace()
dec_filter = torch.topk(hotword_scores, min(nfilter, num_hot_word-1))[1].tolist()
+ pdb.set_trace()
add_filter = dec_filter
+ pdb.set_trace()
add_filter.append(len(hw_list_pad)-1)
# filter hotword embedding
+ pdb.set_trace()
selected = selected[add_filter]
# again
+ pdb.set_trace()
contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
+ pdb.set_trace()
num_hot_word = contextual_info.shape[1]
_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
+ pdb.set_trace()
for dec in self.seaco_decoder.decoders:
dec.attn_mat = []
dec.reserve_attn = False
-
+ pdb.set_trace()
# SeACo Core
cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
+ pdb.set_trace()
dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
+ pdb.set_trace()
merged = self._merge(cif_attended, dec_attended)
-
+ pdb.set_trace()
+
dha_output = self.hotword_output_layer(merged) # remove the last token in loss calculation
+ pdb.set_trace()
dha_pred = torch.log_softmax(dha_output, dim=-1)
+ pdb.set_trace()
def _merge_res(dec_output, dha_output):
+ pdb.set_trace()
lmbd = torch.Tensor([seaco_weight] * dha_output.shape[0])
+ pdb.set_trace()
dha_ids = dha_output.max(-1)[-1]# [0]
+ pdb.set_trace()
dha_mask = (dha_ids == 8377).int().unsqueeze(-1)
+ pdb.set_trace()
a = (1 - lmbd) / lmbd
b = 1 / lmbd
+ pdb.set_trace()
a, b = a.to(dec_output.device), b.to(dec_output.device)
+ pdb.set_trace()
dha_mask = (dha_mask + a.reshape(-1, 1, 1)) / b.reshape(-1, 1, 1)
# logits = dec_output * dha_mask + dha_output[:,:,:-1] * (1-dha_mask)
+ pdb.set_trace()
logits = dec_output * dha_mask + dha_output[:,:,:] * (1-dha_mask)
return logits
+
merged_pred = _merge_res(decoder_pred, dha_pred)
+ pdb.set_trace()
# import pdb; pdb.set_trace()
return merged_pred
else:
@@ -320,7 +347,7 @@
logging.info("enable beam_search")
self.init_beam_search(**kwargs)
self.nbest = kwargs.get("nbest", 1)
-
+ pdb.set_trace()
meta_data = {}
# extract fbank feats
@@ -328,6 +355,7 @@
audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
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()
@@ -337,15 +365,19 @@
speech = speech.to(device=kwargs["device"])
speech_lengths = speech_lengths.to(device=kwargs["device"])
-
+
+ pdb.set_trace()
# hotword
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, _, _ = predictor_outs[0], predictor_outs[1], \
@@ -354,15 +386,16 @@
if torch.max(pre_token_length) < 1:
return []
-
+ pdb.set_trace()
decoder_out = self._seaco_decode_with_ASF(encoder_out, encoder_out_lens,
pre_acoustic_embeds,
pre_token_length,
hw_list=self.hotword_list)
+ pdb.set_trace()
# decoder_out, _ = decoder_outs[0], decoder_outs[1]
_, _, us_alphas, us_peaks = self.calc_predictor_timestamp(encoder_out, encoder_out_lens,
pre_token_length)
-
+ pdb.set_trace()
results = []
b, n, d = decoder_out.size()
for i in range(b):
@@ -387,9 +420,11 @@
nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
for nbest_idx, hyp in enumerate(nbest_hyps):
ibest_writer = None
- if ibest_writer is None and kwargs.get("output_dir") is not None:
- writer = DatadirWriter(kwargs.get("output_dir"))
- ibest_writer = writer[f"{nbest_idx + 1}best_recog"]
+ if kwargs.get("output_dir") is not None:
+ if not hasattr(self, "writer"):
+ self.writer = DatadirWriter(kwargs.get("output_dir"))
+ ibest_writer = self.writer[f"{nbest_idx + 1}best_recog"]
+
# remove sos/eos and get results
last_pos = -1
if isinstance(hyp.yseq, list):
@@ -415,12 +450,11 @@
token, timestamp)
result_i = {"key": key[i], "text": text_postprocessed,
- "timestamp": time_stamp_postprocessed,
+ "timestamp": time_stamp_postprocessed
}
if ibest_writer is not None:
ibest_writer["token"][key[i]] = " ".join(token)
- # ibest_writer["text"][key[i]] = text
ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed
ibest_writer["text"][key[i]] = text_postprocessed
else:
--
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