From cfe577f16fef9fb5b0a48f07d4f9e232799cc9d4 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 08 五月 2024 00:03:52 +0800
Subject: [PATCH] decoding key
---
funasr/models/sense_voice/model.py | 269 ++++++++++++++++++++++++++++++++++++++++++++++++-----
1 files changed, 241 insertions(+), 28 deletions(-)
diff --git a/funasr/models/sense_voice/model.py b/funasr/models/sense_voice/model.py
index c12107e..0230638 100644
--- a/funasr/models/sense_voice/model.py
+++ b/funasr/models/sense_voice/model.py
@@ -15,6 +15,7 @@
from funasr.train_utils.device_funcs import force_gatherable
from . import whisper_lib as whisper
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+from funasr.utils.datadir_writer import DatadirWriter
from funasr.register import tables
@@ -377,14 +378,19 @@
stats = {}
# 1. Forward decoder
+ # ys_pad: [sos, task, lid, text, eos]
decoder_out = self.model.decoder(
x=ys_pad, xa=encoder_out, hlens=encoder_out_lens, ys_in_lens=ys_pad_lens
)
# 2. Compute attention loss
- mask = torch.ones_like(ys_pad) * (-1)
- ys_pad_mask = (ys_pad * target_mask + mask * (1 - target_mask)).to(torch.int64)
- ys_pad_mask[ys_pad_mask == 0] = -1
+ mask = torch.ones_like(ys_pad) * (-1) # [sos, task, lid, text, eos]: [-1, -1, -1, -1]
+ ys_pad_mask = (ys_pad * target_mask + mask * (1 - target_mask)).to(
+ torch.int64
+ ) # [sos, task, lid, text, eos]: [0, 0, 1, 1, 1] + [-1, -1, 0, 0, 0]
+ ys_pad_mask[ys_pad_mask == 0] = -1 # [-1, -1, lid, text, eos]
+ # decoder_out: [sos, task, lid, text]
+ # ys_pad_mask: [-1, lid, text, eos]
loss_att = self.criterion_att(decoder_out[:, :-1, :], ys_pad_mask[:, 1:])
with torch.no_grad():
@@ -394,6 +400,42 @@
)
return loss_att, acc_att, None, None
+
+ def init_beam_search(
+ self,
+ **kwargs,
+ ):
+ from .search import BeamSearch
+
+ from funasr.models.transformer.scorers.length_bonus import LengthBonus
+
+ # 1. Build ASR model
+ scorers = {}
+
+ scorers.update(
+ decoder=self.model.decoder,
+ length_bonus=LengthBonus(self.vocab_size),
+ )
+
+ weights = dict(
+ decoder=1.0,
+ ctc=0.0,
+ lm=0.0,
+ ngram=0.0,
+ length_bonus=kwargs.get("penalty", 0.0),
+ )
+ beam_search = BeamSearch(
+ beam_size=kwargs.get("beam_size", 5),
+ weights=weights,
+ scorers=scorers,
+ sos=None,
+ eos=None,
+ vocab_size=self.vocab_size,
+ token_list=None,
+ pre_beam_score_key="full",
+ )
+
+ self.beam_search = beam_search
def inference(
self,
@@ -406,6 +448,12 @@
):
if kwargs.get("batch_size", 1) > 1:
raise NotImplementedError("batch decoding is not implemented")
+
+ # init beamsearch
+ if not hasattr(self, "beam_search") or self.beam_search is None:
+ logging.info("enable beam_search")
+ self.init_beam_search(**kwargs)
+ self.nbest = kwargs.get("nbest", 1)
if frontend is None and not hasattr(self, "frontend"):
frontend_class = tables.frontend_classes.get("WhisperFrontend")
@@ -455,25 +503,65 @@
task = [task]
task = "".join([f"<|{x}|>" for x in task])
initial_prompt = kwargs.get("initial_prompt", f"<|startoftranscript|>{task}")
- DecodingOptions["initial_prompt"] = initial_prompt
language = DecodingOptions.get("language", None)
language = None if language == "auto" else language
- DecodingOptions["language"] = language
- DecodingOptions["vocab_path"] = kwargs["tokenizer_conf"].get("vocab_path", None)
+ sos = f"{initial_prompt}<|{language}|>" if language is not None else initial_prompt
+ sos_int = tokenizer.encode(sos, allowed_special="all")
+ eos = kwargs.get("model_conf").get("eos")
+ eos_int = tokenizer.encode(eos, allowed_special="all")
+ self.beam_search.sos = sos_int
+ self.beam_search.eos = eos_int[0]
- if "without_timestamps" not in DecodingOptions:
- DecodingOptions["without_timestamps"] = True
+ encoder_out, encoder_out_lens = self.encode(
+ speech[None, :, :].permute(0, 2, 1), speech_lengths
+ )
- options = whisper.DecodingOptions(**DecodingOptions)
+ # c. Passed the encoder result and the beam search
+ nbest_hyps = self.beam_search(
+ x=encoder_out[0],
+ maxlenratio=kwargs.get("maxlenratio", 0.0),
+ minlenratio=kwargs.get("minlenratio", 0.0),
+ )
- result = whisper.decode(self.model, speech, options)
- text = f"{result.text}"
+ nbest_hyps = nbest_hyps[: self.nbest]
+
results = []
- result_i = {"key": key[0], "text": text}
+ b, n, d = encoder_out.size()
+ for i in range(b):
- results.append(result_i)
+ for nbest_idx, hyp in enumerate(nbest_hyps):
+ ibest_writer = None
+ 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):
+ token_int = hyp.yseq[1:last_pos]
+ else:
+ token_int = hyp.yseq[1:last_pos].tolist()
+
+ # # remove blank symbol id, which is assumed to be 0
+ # token_int = list(
+ # filter(
+ # lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int
+ # )
+ # )
+
+ # Change integer-ids to tokens
+ # token = tokenizer.ids2tokens(token_int)
+ text = tokenizer.decode(token_int)
+
+ result_i = {"key": key[i], "text": text}
+ results.append(result_i)
+
+ if ibest_writer is not None:
+ # ibest_writer["token"][key[i]] = " ".join(token)
+ ibest_writer["text"][key[i]] = text
return results, meta_data
@@ -497,12 +585,14 @@
# decoder
del model.decoder
decoder = kwargs.get("decoder", "SenseVoiceDecoder")
- decoder_conf = kwargs.get("decoder_conf", {})
decoder_class = tables.decoder_classes.get(decoder)
decoder = decoder_class(
- vocab_size=dims.n_vocab,
- encoder_output_size=dims.n_audio_state,
- **decoder_conf,
+ n_vocab=dims.n_vocab,
+ n_ctx=dims.n_text_ctx,
+ n_state=dims.n_text_state,
+ n_head=dims.n_text_head,
+ n_layer=dims.n_text_layer,
+ **kwargs.get("decoder_conf"),
)
model.decoder = decoder
@@ -512,7 +602,7 @@
self.activation_checkpoint = kwargs.get("activation_checkpoint", False)
self.ignore_id = kwargs.get("ignore_id", -1)
- self.vocab_size = kwargs.get("vocab_size", -1)
+ self.vocab_size = dims.n_vocab
self.length_normalized_loss = kwargs.get("length_normalized_loss", True)
self.criterion_att = LabelSmoothingLoss(
size=self.vocab_size,
@@ -630,6 +720,42 @@
return loss_att, acc_att, None, None
+ def init_beam_search(
+ self,
+ **kwargs,
+ ):
+ from .search import BeamSearch
+
+ from funasr.models.transformer.scorers.length_bonus import LengthBonus
+
+ # 1. Build ASR model
+ scorers = {}
+
+ scorers.update(
+ decoder=self.model.decoder,
+ length_bonus=LengthBonus(self.vocab_size),
+ )
+
+ weights = dict(
+ decoder=1.0,
+ ctc=0.0,
+ lm=0.0,
+ ngram=0.0,
+ length_bonus=kwargs.get("penalty", 0.0),
+ )
+ beam_search = BeamSearch(
+ beam_size=kwargs.get("beam_size", 5),
+ weights=weights,
+ scorers=scorers,
+ sos=None,
+ eos=None,
+ vocab_size=self.vocab_size,
+ token_list=None,
+ pre_beam_score_key="full",
+ )
+
+ self.beam_search = beam_search
+
def inference(
self,
data_in,
@@ -641,6 +767,12 @@
):
if kwargs.get("batch_size", 1) > 1:
raise NotImplementedError("batch decoding is not implemented")
+
+ # init beamsearch
+ if not hasattr(self, "beam_search") or self.beam_search is None:
+ logging.info("enable beam_search")
+ self.init_beam_search(**kwargs)
+ self.nbest = kwargs.get("nbest", 1)
if frontend is None and not hasattr(self, "frontend"):
frontend_class = tables.frontend_classes.get("WhisperFrontend")
@@ -670,6 +802,16 @@
data_type=kwargs.get("data_type", "sound"),
tokenizer=tokenizer,
)
+
+ if (
+ isinstance(kwargs.get("data_type", None), (list, tuple))
+ and len(kwargs.get("data_type", [])) > 1
+ ):
+ audio_sample_list, text_token_int_list = audio_sample_list
+ text_token_int = text_token_int_list[0]
+ else:
+ text_token_int = None
+
time2 = time.perf_counter()
meta_data["load_data"] = f"{time2 - time1:0.3f}"
speech, speech_lengths = extract_fbank(
@@ -690,24 +832,95 @@
task = [task]
task = "".join([f"<|{x}|>" for x in task])
initial_prompt = kwargs.get("initial_prompt", f"<|startoftranscript|>{task}")
- DecodingOptions["initial_prompt"] = initial_prompt
language = DecodingOptions.get("language", None)
language = None if language == "auto" else language
- DecodingOptions["language"] = language
- DecodingOptions["vocab_path"] = kwargs["tokenizer_conf"].get("vocab_path", None)
+ sos = f"{initial_prompt}<|{language}|>" if language is not None else initial_prompt
+ sos_int = tokenizer.encode(sos, allowed_special="all")
+ eos = kwargs.get("model_conf").get("eos")
+ eos_int = tokenizer.encode(eos, allowed_special="all")
+ self.beam_search.sos = sos_int
+ self.beam_search.eos = eos_int[0]
- if "without_timestamps" not in DecodingOptions:
- DecodingOptions["without_timestamps"] = True
+ encoder_out, encoder_out_lens = self.encode(
+ speech[None, :, :].permute(0, 2, 1), speech_lengths
+ )
- options = whisper.DecodingOptions(**DecodingOptions)
+ if text_token_int is not None:
+ i = 0
+ results = []
+ ibest_writer = None
+ 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"1best_recog"]
- result = whisper.decode(self.model, speech, options)
- text = f"{result.text}"
+ # 1. Forward decoder
+ ys_pad = torch.tensor(sos_int + text_token_int, dtype=torch.int64).to(kwargs["device"])[
+ None, :
+ ]
+ ys_pad_lens = torch.tensor([len(sos_int + text_token_int)], dtype=torch.int64).to(
+ kwargs["device"]
+ )[None, :]
+ decoder_out = self.model.decoder(
+ x=ys_pad, xa=encoder_out, hlens=encoder_out_lens, ys_in_lens=ys_pad_lens
+ )
+
+ token_int = decoder_out.argmax(-1)[0, :].tolist()
+ text = tokenizer.decode(token_int)
+
+ result_i = {"key": key[i], "text": text}
+ results.append(result_i)
+
+ if ibest_writer is not None:
+ # ibest_writer["token"][key[i]] = " ".join(token)
+ ibest_writer["text"][key[i]] = text
+ return results, meta_data
+
+ # c. Passed the encoder result and the beam search
+ nbest_hyps = self.beam_search(
+ x=encoder_out[0],
+ maxlenratio=kwargs.get("maxlenratio", 0.0),
+ minlenratio=kwargs.get("minlenratio", 0.0),
+ )
+
+ nbest_hyps = nbest_hyps[: self.nbest]
+
results = []
- result_i = {"key": key[0], "text": text}
+ b, n, d = encoder_out.size()
+ for i in range(b):
- results.append(result_i)
+ for nbest_idx, hyp in enumerate(nbest_hyps):
+ ibest_writer = None
+ 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):
+ token_int = hyp.yseq[1:last_pos]
+ else:
+ token_int = hyp.yseq[1:last_pos].tolist()
+
+ # # remove blank symbol id, which is assumed to be 0
+ # token_int = list(
+ # filter(
+ # lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int
+ # )
+ # )
+
+ # Change integer-ids to tokens
+ # token = tokenizer.ids2tokens(token_int)
+ text = tokenizer.decode(token_int)
+
+ result_i = {"key": key[i], "text": text}
+ results.append(result_i)
+
+ if ibest_writer is not None:
+ # ibest_writer["token"][key[i]] = " ".join(token)
+ ibest_writer["text"][key[i]] = text
return results, meta_data
--
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