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语帆 2024-02-21 20:04:01 +08:00
parent 0a7384a1ec
commit 62178770dc
3 changed files with 55 additions and 31 deletions
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6
funasr/auto/auto_model.py
View File

@ -209,14 +209,12 @@ class AutoModel:
kwargs.update(cfg)
model = self.model if model is None else model
model.eval()
pdb.set_trace()
batch_size = kwargs.get("batch_size", 1)
# if kwargs.get("device", "cpu") == "cpu":
# batch_size = 1
key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key)
pdb.set_trace()
speed_stats = {}
asr_result_list = []
@ -225,14 +223,12 @@ class AutoModel:
pbar = tqdm(colour="blue", total=num_samples, dynamic_ncols=True) if not disable_pbar else None
time_speech_total = 0.0
time_escape_total = 0.0
pdb.set_trace()
for beg_idx in range(0, num_samples, batch_size):
pdb.set_trace()
end_idx = min(num_samples, beg_idx + batch_size)
data_batch = data_list[beg_idx:end_idx]
key_batch = key_list[beg_idx:end_idx]
batch = {"data_in": data_batch, "key": key_batch}
pdb.set_trace()
if (end_idx - beg_idx) == 1 and kwargs.get("data_type", None) == "fbank": # fbank
batch["data_in"] = data_batch[0]
batch["data_lengths"] = input_len

29
funasr/models/contextual_paraformer/model.py
View File

@ -102,17 +102,16 @@ class ContextualParaformer(Paraformer):
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()
@ -127,12 +126,11 @@ class ContextualParaformer(Paraformer):
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
@ -170,26 +168,24 @@ class ContextualParaformer(Paraformer):
):
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:
@ -201,7 +197,7 @@ class ContextualParaformer(Paraformer):
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
@ -217,7 +213,7 @@ class ContextualParaformer(Paraformer):
loss_ideal = None
'''
loss_ideal = None
pdb.set_trace()
if decoder_out_1st is None:
decoder_out_1st = decoder_out
# 2. Compute attention loss
@ -294,11 +290,11 @@ class ContextualParaformer(Paraformer):
enforce_sorted=False)
_, (h_n, _) = self.bias_encoder(hw_embed)
hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
pdb.set_trace()
decoder_outs = self.decoder(
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=hw_embed, clas_scale=clas_scale
)
pdb.set_trace()
decoder_out = decoder_outs[0]
decoder_out = torch.log_softmax(decoder_out, dim=-1)
return decoder_out, ys_pad_lens
@ -363,14 +359,11 @@ class ContextualParaformer(Paraformer):
clas_scale=kwargs.get("clas_scale", 1.0))
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),

51
funasr/models/seaco_paraformer/model.py
View File

@ -32,7 +32,7 @@ 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
else:
@ -130,7 +130,7 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
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
@ -212,58 +212,87 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
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:
@ -318,7 +347,7 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
logging.info("enable beam_search")
self.init_beam_search(**kwargs)
self.nbest = kwargs.get("nbest", 1)
pdb.set_trace()
meta_data = {}
# extract fbank feats
@ -326,6 +355,7 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
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()
@ -336,14 +366,18 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
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], \
@ -352,15 +386,16 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
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):