From 37d7764ecf0e8cc1a14f59b8b9cd1c914da8b005 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期日, 21 一月 2024 21:06:52 +0800
Subject: [PATCH] Funasr1.0 (#1277)
---
funasr/frontends/wav_frontend.py | 3
funasr/models/fsmn_vad_streaming/model.py | 76 ++++--
funasr/train_utils/trainer.py | 8
funasr/auto/auto_model.py | 3
funasr/models/scama/model.py | 97 ++++----
funasr/models/scama/beam_search.py | 467 ++++++++++++++++++++++++++++++++++++++++++
funasr/datasets/audio_datasets/datasets.py | 2
7 files changed, 570 insertions(+), 86 deletions(-)
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 3320136..0538f66 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -132,7 +132,8 @@
self.punc_kwargs = punc_kwargs
self.spk_model = spk_model
self.spk_kwargs = spk_kwargs
- self.model_path = kwargs.get("model_path", "./")
+ self.model_path = kwargs.get("model_path")
+
def build_model(self, **kwargs):
diff --git a/funasr/datasets/audio_datasets/datasets.py b/funasr/datasets/audio_datasets/datasets.py
index 5af33fc..ebb72a3 100644
--- a/funasr/datasets/audio_datasets/datasets.py
+++ b/funasr/datasets/audio_datasets/datasets.py
@@ -58,7 +58,7 @@
data_src = load_audio_text_image_video(source, fs=self.fs)
if self.preprocessor_speech:
data_src = self.preprocessor_speech(data_src)
- speech, speech_lengths = extract_fbank(data_src, data_type=self.data_type, frontend=self.frontend) # speech: [b, T, d]
+ speech, speech_lengths = extract_fbank(data_src, data_type=self.data_type, frontend=self.frontend, is_final=True) # speech: [b, T, d]
target = item["target"]
if self.preprocessor_text:
diff --git a/funasr/frontends/wav_frontend.py b/funasr/frontends/wav_frontend.py
index 9c896f1..c6e03e8 100644
--- a/funasr/frontends/wav_frontend.py
+++ b/funasr/frontends/wav_frontend.py
@@ -399,9 +399,10 @@
return feats_pad, feats_lens, lfr_splice_frame_idxs
def forward(
- self, input: torch.Tensor, input_lengths: torch.Tensor, cache: dict = {}, **kwargs
+ self, input: torch.Tensor, input_lengths: torch.Tensor, **kwargs
):
is_final = kwargs.get("is_final", False)
+ cache = kwargs.get("cache", {})
if len(cache) == 0:
self.init_cache(cache)
diff --git a/funasr/models/fsmn_vad_streaming/model.py b/funasr/models/fsmn_vad_streaming/model.py
index becfd56..76eee81 100644
--- a/funasr/models/fsmn_vad_streaming/model.py
+++ b/funasr/models/fsmn_vad_streaming/model.py
@@ -15,7 +15,7 @@
from typing import List, Tuple, Dict, Any, Optional
from funasr.utils.datadir_writer import DatadirWriter
-from funasr.utils.load_utils import load_audio_text_image_video,extract_fbank
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
class VadStateMachine(Enum):
@@ -23,10 +23,12 @@
kVadInStateInSpeechSegment = 2
kVadInStateEndPointDetected = 3
+
class FrameState(Enum):
kFrameStateInvalid = -1
kFrameStateSpeech = 1
kFrameStateSil = 0
+
# final voice/unvoice state per frame
class AudioChangeState(Enum):
@@ -37,9 +39,11 @@
kChangeStateNoBegin = 4
kChangeStateInvalid = 5
+
class VadDetectMode(Enum):
kVadSingleUtteranceDetectMode = 0
kVadMutipleUtteranceDetectMode = 1
+
class VADXOptions:
"""
@@ -47,6 +51,7 @@
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
https://arxiv.org/abs/1803.05030
"""
+
def __init__(
self,
sample_rate: int = 16000,
@@ -117,6 +122,7 @@
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
https://arxiv.org/abs/1803.05030
"""
+
def __init__(self):
self.start_ms = 0
self.end_ms = 0
@@ -140,6 +146,7 @@
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
https://arxiv.org/abs/1803.05030
"""
+
def __init__(self):
self.noise_prob = 0.0
self.speech_prob = 0.0
@@ -154,6 +161,7 @@
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
https://arxiv.org/abs/1803.05030
"""
+
def __init__(self, window_size_ms: int,
sil_to_speech_time: int,
speech_to_sil_time: int,
@@ -190,7 +198,7 @@
def GetWinSize(self) -> int:
return int(self.win_size_frame)
- def DetectOneFrame(self, frameState: FrameState, frame_count: int, cache: dict={}) -> AudioChangeState:
+ def DetectOneFrame(self, frameState: FrameState, frame_count: int, cache: dict = {}) -> AudioChangeState:
cur_frame_state = FrameState.kFrameStateSil
if frameState == FrameState.kFrameStateSpeech:
cur_frame_state = 1
@@ -220,13 +228,13 @@
def FrameSizeMs(self) -> int:
return int(self.frame_size_ms)
+
class Stats(object):
def __init__(self,
sil_pdf_ids,
max_end_sil_frame_cnt_thresh,
speech_noise_thres,
):
-
self.data_buf_start_frame = 0
self.frm_cnt = 0
self.latest_confirmed_speech_frame = 0
@@ -255,6 +263,7 @@
self.waveform = None
self.last_drop_frames = 0
+
@tables.register("model_classes", "FsmnVADStreaming")
class FsmnVADStreaming(nn.Module):
"""
@@ -262,6 +271,7 @@
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
https://arxiv.org/abs/1803.05030
"""
+
def __init__(self,
encoder: str = None,
encoder_conf: Optional[Dict] = None,
@@ -274,7 +284,6 @@
encoder_class = tables.encoder_classes.get(encoder)
encoder = encoder_class(**encoder_conf)
self.encoder = encoder
-
def ResetDetection(self, cache: dict = {}):
cache["stats"].continous_silence_frame_count = 0
@@ -292,7 +301,8 @@
drop_frames = int(cache["stats"].output_data_buf[-1].end_ms / self.vad_opts.frame_in_ms)
real_drop_frames = drop_frames - cache["stats"].last_drop_frames
cache["stats"].last_drop_frames = drop_frames
- cache["stats"].data_buf_all = cache["stats"].data_buf_all[real_drop_frames * int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
+ cache["stats"].data_buf_all = cache["stats"].data_buf_all[real_drop_frames * int(
+ self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
cache["stats"].decibel = cache["stats"].decibel[real_drop_frames:]
cache["stats"].scores = cache["stats"].scores[:, real_drop_frames:, :]
@@ -300,7 +310,8 @@
frame_sample_length = int(self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000)
frame_shift_length = int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000)
if cache["stats"].data_buf_all is None:
- cache["stats"].data_buf_all = cache["stats"].waveform[0] # cache["stats"].data_buf is pointed to cache["stats"].waveform[0]
+ cache["stats"].data_buf_all = cache["stats"].waveform[
+ 0] # cache["stats"].data_buf is pointed to cache["stats"].waveform[0]
cache["stats"].data_buf = cache["stats"].data_buf_all
else:
cache["stats"].data_buf_all = torch.cat((cache["stats"].data_buf_all, cache["stats"].waveform[0]))
@@ -319,15 +330,16 @@
else:
cache["stats"].scores = torch.cat((cache["stats"].scores, scores), dim=1)
- def PopDataBufTillFrame(self, frame_idx: int, cache: dict={}) -> None: # need check again
+ def PopDataBufTillFrame(self, frame_idx: int, cache: dict = {}) -> None: # need check again
while cache["stats"].data_buf_start_frame < frame_idx:
if len(cache["stats"].data_buf) >= int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):
cache["stats"].data_buf_start_frame += 1
- cache["stats"].data_buf = cache["stats"].data_buf_all[(cache["stats"].data_buf_start_frame - cache["stats"].last_drop_frames) * int(
- self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
+ cache["stats"].data_buf = cache["stats"].data_buf_all[
+ (cache["stats"].data_buf_start_frame - cache["stats"].last_drop_frames) * int(
+ self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
def PopDataToOutputBuf(self, start_frm: int, frm_cnt: int, first_frm_is_start_point: bool,
- last_frm_is_end_point: bool, end_point_is_sent_end: bool, cache: dict={}) -> None:
+ last_frm_is_end_point: bool, end_point_is_sent_end: bool, cache: dict = {}) -> None:
self.PopDataBufTillFrame(start_frm, cache=cache)
expected_sample_number = int(frm_cnt * self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000)
if last_frm_is_end_point:
@@ -379,14 +391,15 @@
cache["stats"].lastest_confirmed_silence_frame = valid_frame
if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
self.PopDataBufTillFrame(valid_frame, cache=cache)
- # silence_detected_callback_
- # pass
- def OnVoiceDetected(self, valid_frame: int, cache:dict={}) -> None:
+ # silence_detected_callback_
+ # pass
+
+ def OnVoiceDetected(self, valid_frame: int, cache: dict = {}) -> None:
cache["stats"].latest_confirmed_speech_frame = valid_frame
self.PopDataToOutputBuf(valid_frame, 1, False, False, False, cache=cache)
- def OnVoiceStart(self, start_frame: int, fake_result: bool = False, cache:dict={}) -> None:
+ def OnVoiceStart(self, start_frame: int, fake_result: bool = False, cache: dict = {}) -> None:
if self.vad_opts.do_start_point_detection:
pass
if cache["stats"].confirmed_start_frame != -1:
@@ -397,7 +410,7 @@
if not fake_result and cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
self.PopDataToOutputBuf(cache["stats"].confirmed_start_frame, 1, True, False, False, cache=cache)
- def OnVoiceEnd(self, end_frame: int, fake_result: bool, is_last_frame: bool, cache:dict={}) -> None:
+ def OnVoiceEnd(self, end_frame: int, fake_result: bool, is_last_frame: bool, cache: dict = {}) -> None:
for t in range(cache["stats"].latest_confirmed_speech_frame + 1, end_frame):
self.OnVoiceDetected(t, cache=cache)
if self.vad_opts.do_end_point_detection:
@@ -487,7 +500,8 @@
segment_batch = []
if len(cache["stats"].output_data_buf) > 0:
for i in range(cache["stats"].output_data_buf_offset, len(cache["stats"].output_data_buf)):
- if not is_final and (not cache["stats"].output_data_buf[i].contain_seg_start_point or not cache["stats"].output_data_buf[
+ if not is_final and (not cache["stats"].output_data_buf[i].contain_seg_start_point or not
+ cache["stats"].output_data_buf[
i].contain_seg_end_point):
continue
segment = [cache["stats"].output_data_buf[i].start_ms, cache["stats"].output_data_buf[i].end_ms]
@@ -499,9 +513,9 @@
# # reset class variables and clear the dict for the next query
# self.AllResetDetection()
return segments
-
+
def init_cache(self, cache: dict = {}, **kwargs):
-
+
cache["frontend"] = {}
cache["prev_samples"] = torch.empty(0)
cache["encoder"] = {}
@@ -528,12 +542,12 @@
cache: dict = {},
**kwargs,
):
-
+
if len(cache) == 0:
self.init_cache(cache, **kwargs)
meta_data = {}
- chunk_size = kwargs.get("chunk_size", 60000) # 50ms
+ chunk_size = kwargs.get("chunk_size", 60000) # 50ms
chunk_stride_samples = int(chunk_size * frontend.fs / 1000)
time1 = time.perf_counter()
@@ -580,7 +594,6 @@
if len(segments_i) > 0:
segments.extend(*segments_i)
-
cache["prev_samples"] = audio_sample[:-m]
if _is_final:
self.init_cache(cache)
@@ -600,16 +613,15 @@
if ibest_writer is not None:
ibest_writer["text"][key[0]] = segments
-
return results, meta_data
-
def DetectCommonFrames(self, cache: dict = {}) -> int:
if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
return 0
for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1):
frame_state = FrameState.kFrameStateInvalid
- frame_state = self.GetFrameState(cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames, cache=cache)
+ frame_state = self.GetFrameState(cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames,
+ cache=cache)
self.DetectOneFrame(frame_state, cache["stats"].frm_cnt - 1 - i, False, cache=cache)
return 0
@@ -619,7 +631,8 @@
return 0
for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1):
frame_state = FrameState.kFrameStateInvalid
- frame_state = self.GetFrameState(cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames, cache=cache)
+ frame_state = self.GetFrameState(cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames,
+ cache=cache)
if i != 0:
self.DetectOneFrame(frame_state, cache["stats"].frm_cnt - 1 - i, False, cache=cache)
else:
@@ -627,7 +640,8 @@
return 0
- def DetectOneFrame(self, cur_frm_state: FrameState, cur_frm_idx: int, is_final_frame: bool, cache: dict = {}) -> None:
+ def DetectOneFrame(self, cur_frm_state: FrameState, cur_frm_idx: int, is_final_frame: bool,
+ cache: dict = {}) -> None:
tmp_cur_frm_state = FrameState.kFrameStateInvalid
if cur_frm_state == FrameState.kFrameStateSpeech:
if math.fabs(1.0) > self.vad_opts.fe_prior_thres:
@@ -644,7 +658,8 @@
cache["stats"].pre_end_silence_detected = False
start_frame = 0
if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
- start_frame = max(cache["stats"].data_buf_start_frame, cur_frm_idx - self.LatencyFrmNumAtStartPoint(cache=cache))
+ start_frame = max(cache["stats"].data_buf_start_frame,
+ cur_frm_idx - self.LatencyFrmNumAtStartPoint(cache=cache))
self.OnVoiceStart(start_frame, cache=cache)
cache["stats"].vad_state_machine = VadStateMachine.kVadInStateInSpeechSegment
for t in range(start_frame + 1, cur_frm_idx + 1):
@@ -696,7 +711,8 @@
if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
# silence timeout, return zero length decision
if ((self.vad_opts.detect_mode == VadDetectMode.kVadSingleUtteranceDetectMode.value) and (
- cache["stats"].continous_silence_frame_count * frm_shift_in_ms > self.vad_opts.max_start_silence_time)) \
+ cache[
+ "stats"].continous_silence_frame_count * frm_shift_in_ms > self.vad_opts.max_start_silence_time)) \
or (is_final_frame and cache["stats"].number_end_time_detected == 0):
for t in range(cache["stats"].lastest_confirmed_silence_frame + 1, cur_frm_idx):
self.OnSilenceDetected(t, cache=cache)
@@ -707,7 +723,8 @@
if cur_frm_idx >= self.LatencyFrmNumAtStartPoint(cache=cache):
self.OnSilenceDetected(cur_frm_idx - self.LatencyFrmNumAtStartPoint(cache=cache), cache=cache)
elif cache["stats"].vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
- if cache["stats"].continous_silence_frame_count * frm_shift_in_ms >= cache["stats"].max_end_sil_frame_cnt_thresh:
+ if cache["stats"].continous_silence_frame_count * frm_shift_in_ms >= cache[
+ "stats"].max_end_sil_frame_cnt_thresh:
lookback_frame = int(cache["stats"].max_end_sil_frame_cnt_thresh / frm_shift_in_ms)
if self.vad_opts.do_extend:
lookback_frame -= int(self.vad_opts.lookahead_time_end_point / frm_shift_in_ms)
@@ -731,6 +748,5 @@
if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateEndPointDetected and \
self.vad_opts.detect_mode == VadDetectMode.kVadMutipleUtteranceDetectMode.value:
self.ResetDetection(cache=cache)
-
diff --git a/funasr/models/scama/beam_search.py b/funasr/models/scama/beam_search.py
index 8f0d751..b8aa876 100644
--- a/funasr/models/scama/beam_search.py
+++ b/funasr/models/scama/beam_search.py
@@ -11,7 +11,7 @@
import torch
-from funasr.metrics import end_detect
+from funasr.metrics.common import end_detect
from funasr.models.transformer.scorers.scorer_interface import PartialScorerInterface
from funasr.models.transformer.scorers.scorer_interface import ScorerInterface
@@ -494,3 +494,468 @@
else:
remained_hyps.append(hyp)
return remained_hyps
+
+class BeamSearchScamaStreaming(torch.nn.Module):
+ """Beam search implementation."""
+
+ def __init__(
+ self,
+ scorers: Dict[str, ScorerInterface],
+ weights: Dict[str, float],
+ beam_size: int,
+ vocab_size: int,
+ sos: int,
+ eos: int,
+ token_list: List[str] = None,
+ pre_beam_ratio: float = 1.5,
+ pre_beam_score_key: str = None,
+ ):
+ """Initialize beam search.
+
+ Args:
+ scorers (dict[str, ScorerInterface]): Dict of decoder modules
+ e.g., Decoder, CTCPrefixScorer, LM
+ The scorer will be ignored if it is `None`
+ weights (dict[str, float]): Dict of weights for each scorers
+ The scorer will be ignored if its weight is 0
+ beam_size (int): The number of hypotheses kept during search
+ vocab_size (int): The number of vocabulary
+ sos (int): Start of sequence id
+ eos (int): End of sequence id
+ token_list (list[str]): List of tokens for debug log
+ pre_beam_score_key (str): key of scores to perform pre-beam search
+ pre_beam_ratio (float): beam size in the pre-beam search
+ will be `int(pre_beam_ratio * beam_size)`
+
+ """
+ super().__init__()
+ # set scorers
+ self.weights = weights
+ self.scorers = dict()
+ self.full_scorers = dict()
+ self.part_scorers = dict()
+ # this module dict is required for recursive cast
+ # `self.to(device, dtype)` in `recog.py`
+ self.nn_dict = torch.nn.ModuleDict()
+ for k, v in scorers.items():
+ w = weights.get(k, 0)
+ if w == 0 or v is None:
+ continue
+ assert isinstance(
+ v, ScorerInterface
+ ), f"{k} ({type(v)}) does not implement ScorerInterface"
+ self.scorers[k] = v
+ if isinstance(v, PartialScorerInterface):
+ self.part_scorers[k] = v
+ else:
+ self.full_scorers[k] = v
+ if isinstance(v, torch.nn.Module):
+ self.nn_dict[k] = v
+
+ # set configurations
+ self.sos = sos
+ self.eos = eos
+ self.token_list = token_list
+ self.pre_beam_size = int(pre_beam_ratio * beam_size)
+ self.beam_size = beam_size
+ self.n_vocab = vocab_size
+ if (
+ pre_beam_score_key is not None
+ and pre_beam_score_key != "full"
+ and pre_beam_score_key not in self.full_scorers
+ ):
+ raise KeyError(f"{pre_beam_score_key} is not found in {self.full_scorers}")
+ self.pre_beam_score_key = pre_beam_score_key
+ self.do_pre_beam = (
+ self.pre_beam_score_key is not None
+ and self.pre_beam_size < self.n_vocab
+ and len(self.part_scorers) > 0
+ )
+
+ def init_hyp(self, x) -> List[Hypothesis]:
+ """Get an initial hypothesis data.
+
+ Args:
+ x (torch.Tensor): The encoder output feature
+
+ Returns:
+ Hypothesis: The initial hypothesis.
+
+ """
+ init_states = dict()
+ init_scores = dict()
+ for k, d in self.scorers.items():
+ init_states[k] = d.init_state(x)
+ init_scores[k] = 0.0
+ return [
+ Hypothesis(
+ score=0.0,
+ scores=init_scores,
+ states=init_states,
+ yseq=torch.tensor([self.sos], device=x.device),
+ )
+ ]
+
+ @staticmethod
+ def append_token(xs: torch.Tensor, x: int) -> torch.Tensor:
+ """Append new token to prefix tokens.
+
+ Args:
+ xs (torch.Tensor): The prefix token
+ x (int): The new token to append
+
+ Returns:
+ torch.Tensor: New tensor contains: xs + [x] with xs.dtype and xs.device
+
+ """
+ x = torch.tensor([x], dtype=xs.dtype, device=xs.device)
+ return torch.cat((xs, x))
+
+ def score_full(
+ self, hyp: Hypothesis,
+ x: torch.Tensor,
+ x_mask: torch.Tensor = None,
+ pre_acoustic_embeds: torch.Tensor = None,
+ cache: dict={},
+ ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]:
+ """Score new hypothesis by `self.full_scorers`.
+
+ Args:
+ hyp (Hypothesis): Hypothesis with prefix tokens to score
+ x (torch.Tensor): Corresponding input feature
+
+ Returns:
+ Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of
+ score dict of `hyp` that has string keys of `self.full_scorers`
+ and tensor score values of shape: `(self.n_vocab,)`,
+ and state dict that has string keys
+ and state values of `self.full_scorers`
+
+ """
+ scores = dict()
+ states = dict()
+ for k, d in self.full_scorers.items():
+ scores[k], states[k] = d.score(hyp.yseq, hyp.states[k], x, x_mask=x_mask, pre_acoustic_embeds=pre_acoustic_embeds, cache=cache)
+ return scores, states
+
+ def score_partial(
+ self, hyp: Hypothesis, ids: torch.Tensor, x: torch.Tensor
+ ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]:
+ """Score new hypothesis by `self.part_scorers`.
+
+ Args:
+ hyp (Hypothesis): Hypothesis with prefix tokens to score
+ ids (torch.Tensor): 1D tensor of new partial tokens to score
+ x (torch.Tensor): Corresponding input feature
+
+ Returns:
+ Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of
+ score dict of `hyp` that has string keys of `self.part_scorers`
+ and tensor score values of shape: `(len(ids),)`,
+ and state dict that has string keys
+ and state values of `self.part_scorers`
+
+ """
+ scores = dict()
+ states = dict()
+ for k, d in self.part_scorers.items():
+ scores[k], states[k] = d.score_partial(hyp.yseq, ids, hyp.states[k], x)
+ return scores, states
+
+ def beam(
+ self, weighted_scores: torch.Tensor, ids: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Compute topk full token ids and partial token ids.
+
+ Args:
+ weighted_scores (torch.Tensor): The weighted sum scores for each tokens.
+ Its shape is `(self.n_vocab,)`.
+ ids (torch.Tensor): The partial token ids to compute topk
+
+ Returns:
+ Tuple[torch.Tensor, torch.Tensor]:
+ The topk full token ids and partial token ids.
+ Their shapes are `(self.beam_size,)`
+
+ """
+ # no pre beam performed
+ if weighted_scores.size(0) == ids.size(0):
+ top_ids = weighted_scores.topk(self.beam_size)[1]
+ return top_ids, top_ids
+
+ # mask pruned in pre-beam not to select in topk
+ tmp = weighted_scores[ids]
+ weighted_scores[:] = -float("inf")
+ weighted_scores[ids] = tmp
+ top_ids = weighted_scores.topk(self.beam_size)[1]
+ local_ids = weighted_scores[ids].topk(self.beam_size)[1]
+ return top_ids, local_ids
+
+ @staticmethod
+ def merge_scores(
+ prev_scores: Dict[str, float],
+ next_full_scores: Dict[str, torch.Tensor],
+ full_idx: int,
+ next_part_scores: Dict[str, torch.Tensor],
+ part_idx: int,
+ ) -> Dict[str, torch.Tensor]:
+ """Merge scores for new hypothesis.
+
+ Args:
+ prev_scores (Dict[str, float]):
+ The previous hypothesis scores by `self.scorers`
+ next_full_scores (Dict[str, torch.Tensor]): scores by `self.full_scorers`
+ full_idx (int): The next token id for `next_full_scores`
+ next_part_scores (Dict[str, torch.Tensor]):
+ scores of partial tokens by `self.part_scorers`
+ part_idx (int): The new token id for `next_part_scores`
+
+ Returns:
+ Dict[str, torch.Tensor]: The new score dict.
+ Its keys are names of `self.full_scorers` and `self.part_scorers`.
+ Its values are scalar tensors by the scorers.
+
+ """
+ new_scores = dict()
+ for k, v in next_full_scores.items():
+ new_scores[k] = prev_scores[k] + v[full_idx]
+ for k, v in next_part_scores.items():
+ new_scores[k] = prev_scores[k] + v[part_idx]
+ return new_scores
+
+ def merge_states(self, states: Any, part_states: Any, part_idx: int) -> Any:
+ """Merge states for new hypothesis.
+
+ Args:
+ states: states of `self.full_scorers`
+ part_states: states of `self.part_scorers`
+ part_idx (int): The new token id for `part_scores`
+
+ Returns:
+ Dict[str, torch.Tensor]: The new score dict.
+ Its keys are names of `self.full_scorers` and `self.part_scorers`.
+ Its values are states of the scorers.
+
+ """
+ new_states = dict()
+ for k, v in states.items():
+ new_states[k] = v
+ for k, d in self.part_scorers.items():
+ new_states[k] = d.select_state(part_states[k], part_idx)
+ return new_states
+
+ def search(
+ self, running_hyps: List[Hypothesis],
+ x: torch.Tensor,
+ x_mask: torch.Tensor = None,
+ pre_acoustic_embeds: torch.Tensor = None,
+ cache: dict={},
+ ) -> List[Hypothesis]:
+ """Search new tokens for running hypotheses and encoded speech x.
+
+ Args:
+ running_hyps (List[Hypothesis]): Running hypotheses on beam
+ x (torch.Tensor): Encoded speech feature (T, D)
+
+ Returns:
+ List[Hypotheses]: Best sorted hypotheses
+
+ """
+ best_hyps = []
+ part_ids = torch.arange(self.n_vocab, device=x.device) # no pre-beam
+ for hyp in running_hyps:
+ # scoring
+ weighted_scores = torch.zeros(self.n_vocab, dtype=x.dtype, device=x.device)
+ scores, states = self.score_full(hyp, x, x_mask=x_mask, pre_acoustic_embeds=pre_acoustic_embeds, cache=cache)
+ for k in self.full_scorers:
+ weighted_scores += self.weights[k] * scores[k]
+ # partial scoring
+ if self.do_pre_beam:
+ pre_beam_scores = (
+ weighted_scores
+ if self.pre_beam_score_key == "full"
+ else scores[self.pre_beam_score_key]
+ )
+ part_ids = torch.topk(pre_beam_scores, self.pre_beam_size)[1]
+ part_scores, part_states = self.score_partial(hyp, part_ids, x)
+ for k in self.part_scorers:
+ weighted_scores[part_ids] += self.weights[k] * part_scores[k]
+ # add previous hyp score
+ weighted_scores += hyp.score
+
+ # update hyps
+ for j, part_j in zip(*self.beam(weighted_scores, part_ids)):
+ # will be (2 x beam at most)
+ best_hyps.append(
+ Hypothesis(
+ score=weighted_scores[j],
+ yseq=self.append_token(hyp.yseq, j),
+ scores=self.merge_scores(
+ hyp.scores, scores, j, part_scores, part_j
+ ),
+ states=self.merge_states(states, part_states, part_j),
+ )
+ )
+
+ # sort and prune 2 x beam -> beam
+ best_hyps = sorted(best_hyps, key=lambda x: x.score, reverse=True)[
+ : min(len(best_hyps), self.beam_size)
+ ]
+ return best_hyps
+
+ def forward(
+ self, x: torch.Tensor,
+ scama_mask: torch.Tensor = None,
+ pre_acoustic_embeds: torch.Tensor = None,
+ maxlenratio: float = 0.0,
+ minlenratio: float = 0.0,
+ maxlen: int = None,
+ minlen: int = 0,
+ cache:dict={},
+ ) -> List[Hypothesis]:
+ """Perform beam search.
+
+ Args:
+ x (torch.Tensor): Encoded speech feature (T, D)
+ maxlenratio (float): Input length ratio to obtain max output length.
+ If maxlenratio=0.0 (default), it uses a end-detect function
+ to automatically find maximum hypothesis lengths
+ If maxlenratio<0.0, its absolute value is interpreted
+ as a constant max output length.
+ minlenratio (float): Input length ratio to obtain min output length.
+
+ Returns:
+ list[Hypothesis]: N-best decoding results
+
+ """
+ if maxlen is None:
+ # set length bounds
+ if maxlenratio == 0:
+ maxlen = x.shape[0]
+ elif maxlenratio < 0:
+ maxlen = -1 * int(maxlenratio)
+ else:
+ maxlen = max(1, int(maxlenratio * x.size(0)))
+ minlen = int(minlenratio * x.size(0))
+
+ logging.info("decoder input length: " + str(x.shape[0]))
+ logging.info("max output length: " + str(maxlen))
+ logging.info("min output length: " + str(minlen))
+
+ # main loop of prefix search
+ # running_hyps = self.init_hyp(x)
+ running_hyps = cache["running_hyps"]
+ ended_hyps = []
+ for i in range(maxlen):
+ logging.debug("position " + str(i))
+ mask_enc = None
+ # if scama_mask is not None:
+ # token_num_predictor = scama_mask.size(1)
+ # token_id_slice = min(i, token_num_predictor-1)
+ # mask_enc = scama_mask[:, token_id_slice:token_id_slice+1, :]
+ # # if mask_enc.size(1) == 0:
+ # # mask_enc = scama_mask[:, -2:-1, :]
+ # # # mask_enc = torch.zeros_like(mask_enc)
+ pre_acoustic_embeds_cur = None
+ if pre_acoustic_embeds is not None:
+ b, t, d = pre_acoustic_embeds.size()
+ pad = torch.zeros((b, 1, d), dtype=pre_acoustic_embeds.dtype).to(device=pre_acoustic_embeds.device)
+ pre_acoustic_embeds = torch.cat((pre_acoustic_embeds, pad), dim=1)
+ token_id_slice = min(i, t)
+ pre_acoustic_embeds_cur = pre_acoustic_embeds[:, token_id_slice:token_id_slice+1, :]
+
+ best = self.search(running_hyps, x, x_mask=mask_enc, pre_acoustic_embeds=pre_acoustic_embeds_cur, cache=cache["decoder"])
+ # post process of one iteration
+ running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps)
+ # end detection
+ if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i):
+ logging.info(f"end detected at {i}")
+ break
+ if len(running_hyps) == 0:
+ logging.info("no hypothesis. Finish decoding.")
+ break
+ else:
+ logging.debug(f"remained hypotheses: {len(running_hyps)}")
+
+ nbest_hyps = sorted(ended_hyps, key=lambda x: x.score, reverse=True)
+ # check the number of hypotheses reaching to eos
+ if len(nbest_hyps) == 0:
+ logging.warning(
+ "there is no N-best results, perform recognition "
+ "again with smaller minlenratio."
+ )
+ return (
+ []
+ if minlenratio < 0.1
+ else self.forward(x, maxlenratio, max(0.0, minlenratio - 0.1))
+ )
+
+ # report the best result
+ for x in nbest_hyps:
+ yseq = "".join([self.token_list[x] for x in x.yseq])
+ logging.debug("nbest: y: {}, yseq: {}, score: {}".format(x.yseq, yseq, x.score))
+ best = nbest_hyps[0]
+ for k, v in best.scores.items():
+ logging.info(
+ f"{v:6.2f} * {self.weights[k]:3} = {v * self.weights[k]:6.2f} for {k}"
+ )
+ logging.info(f"total log probability: {best.score:.2f}")
+ logging.info(f"normalized log probability: {best.score / len(best.yseq):.2f}")
+ logging.info(f"total number of ended hypotheses: {len(nbest_hyps)}")
+ if self.token_list is not None:
+ logging.info(
+ "best hypo: "
+ + "".join([self.token_list[x] for x in best.yseq[1:-1]])
+ + "\n"
+ )
+ return nbest_hyps
+
+ def post_process(
+ self,
+ i: int,
+ maxlen: int,
+ maxlenratio: float,
+ running_hyps: List[Hypothesis],
+ ended_hyps: List[Hypothesis],
+ ) -> List[Hypothesis]:
+ """Perform post-processing of beam search iterations.
+
+ Args:
+ i (int): The length of hypothesis tokens.
+ maxlen (int): The maximum length of tokens in beam search.
+ maxlenratio (int): The maximum length ratio in beam search.
+ running_hyps (List[Hypothesis]): The running hypotheses in beam search.
+ ended_hyps (List[Hypothesis]): The ended hypotheses in beam search.
+
+ Returns:
+ List[Hypothesis]: The new running hypotheses.
+
+ """
+ logging.debug(f"the number of running hypotheses: {len(running_hyps)}")
+ if self.token_list is not None:
+ logging.debug(
+ "best hypo: "
+ + "".join([self.token_list[x] for x in running_hyps[0].yseq[1:]])
+ )
+ # add eos in the final loop to avoid that there are no ended hyps
+ if i == maxlen - 1:
+ logging.info("adding <eos> in the last position in the loop")
+ running_hyps = [
+ h._replace(yseq=self.append_token(h.yseq, self.eos))
+ for h in running_hyps
+ ]
+
+ # add ended hypotheses to a final list, and removed them from current hypotheses
+ # (this will be a problem, number of hyps < beam)
+ remained_hyps = []
+ for hyp in running_hyps:
+ if hyp.yseq[-1] == self.eos:
+ # e.g., Word LM needs to add final <eos> score
+ for k, d in chain(self.full_scorers.items(), self.part_scorers.items()):
+ s = d.final_score(hyp.states[k])
+ hyp.scores[k] += s
+ hyp = hyp._replace(score=hyp.score + self.weights[k] * s)
+ ended_hyps.append(hyp)
+ else:
+ remained_hyps.append(hyp)
+ return remained_hyps
diff --git a/funasr/models/scama/model.py b/funasr/models/scama/model.py
index aec6fe3..32e16bd 100644
--- a/funasr/models/scama/model.py
+++ b/funasr/models/scama/model.py
@@ -436,7 +436,10 @@
def init_beam_search(self,
**kwargs,
):
- from funasr.models.scama.beam_search import BeamSearchScama
+
+ from funasr.models.scama.beam_search import BeamSearchScamaStreaming
+
+
from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
from funasr.models.transformer.scorers.length_bonus import LengthBonus
@@ -460,13 +463,14 @@
scorers["ngram"] = ngram
weights = dict(
- decoder=1.0 - kwargs.get("decoding_ctc_weight"),
+ decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.0),
ctc=kwargs.get("decoding_ctc_weight", 0.0),
lm=kwargs.get("lm_weight", 0.0),
ngram=kwargs.get("ngram_weight", 0.0),
length_bonus=kwargs.get("penalty", 0.0),
)
- beam_search = BeamSearchScama(
+
+ beam_search = BeamSearchScamaStreaming(
beam_size=kwargs.get("beam_size", 2),
weights=weights,
scorers=scorers,
@@ -499,7 +503,11 @@
is_final=kwargs.get("is_final", False))
if isinstance(encoder_out, tuple):
encoder_out = encoder_out[0]
-
+ if "running_hyps" not in cache:
+ running_hyps = self.beam_search.init_hyp(encoder_out)
+ cache["running_hyps"] = running_hyps
+
+
# predictor
predictor_outs = self.calc_predictor_chunk(encoder_out,
encoder_out_lens,
@@ -513,47 +521,30 @@
if torch.max(pre_token_length) < 1:
return []
- decoder_outs = self.cal_decoder_with_predictor_chunk(encoder_out,
- encoder_out_lens,
- pre_acoustic_embeds,
- pre_token_length,
- cache=cache
- )
- decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
-
+ maxlen = minlen = pre_token_length
+ if kwargs.get("is_final", False):
+ maxlen += kwargs.get("token_num_relax", 5)
+ minlen = max(0, minlen - kwargs.get("token_num_relax", 5))
+ # c. Passed the encoder result and the beam search
+ nbest_hyps = self.beam_search(
+ x=encoder_out[0], scama_mask=None, pre_acoustic_embeds=pre_acoustic_embeds, maxlen=int(maxlen), minlen=int(minlen), cache=cache,
+ )
+
+ cache["running_hyps"] = nbest_hyps
+ nbest_hyps = nbest_hyps[: self.nbest]
+
results = []
- b, n, d = decoder_out.size()
- if isinstance(key[0], (list, tuple)):
- key = key[0]
- for i in range(b):
- x = encoder_out[i, :encoder_out_lens[i], :]
- am_scores = decoder_out[i, :pre_token_length[i], :]
- if self.beam_search is not None:
- nbest_hyps = self.beam_search(
- x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0),
- minlenratio=kwargs.get("minlenratio", 0.0)
- )
-
- nbest_hyps = nbest_hyps[: self.nbest]
+ for hyp in nbest_hyps:
+ # assert isinstance(hyp, (Hypothesis)), type(hyp)
+
+ # remove sos/eos and get results
+ last_pos = -1
+ if isinstance(hyp.yseq, list):
+ token_int = hyp.yseq[1:last_pos]
else:
-
- yseq = am_scores.argmax(dim=-1)
- score = am_scores.max(dim=-1)[0]
- score = torch.sum(score, dim=-1)
- # pad with mask tokens to ensure compatibility with sos/eos tokens
- yseq = torch.tensor(
- [self.sos] + yseq.tolist() + [self.eos], device=yseq.device
- )
- nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
- for nbest_idx, hyp in enumerate(nbest_hyps):
-
- # 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()
-
+ 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))
@@ -568,6 +559,8 @@
return results
def init_cache(self, cache: dict = {}, **kwargs):
+ device = kwargs.get("device", "cuda")
+
chunk_size = kwargs.get("chunk_size", [0, 10, 5])
encoder_chunk_look_back = kwargs.get("encoder_chunk_look_back", 0)
decoder_chunk_look_back = kwargs.get("decoder_chunk_look_back", 0)
@@ -575,10 +568,11 @@
enc_output_size = kwargs["encoder_conf"]["output_size"]
feats_dims = kwargs["frontend_conf"]["n_mels"] * kwargs["frontend_conf"]["lfr_m"]
- cache_encoder = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
- "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size,
+
+ cache_encoder = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)).to(device=device),
+ "cif_alphas": torch.zeros((batch_size, 1)).to(device=device), "chunk_size": chunk_size,
"encoder_chunk_look_back": encoder_chunk_look_back, "last_chunk": False, "opt": None,
- "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)),
+ "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)).to(device=device),
"tail_chunk": False}
cache["encoder"] = cache_encoder
@@ -586,8 +580,10 @@
"chunk_size": chunk_size}
cache["decoder"] = cache_decoder
cache["frontend"] = {}
- cache["prev_samples"] = torch.empty(0)
-
+
+
+ cache["prev_samples"] = torch.empty(0).to(device=device)
+
return cache
def inference(self,
@@ -603,7 +599,10 @@
# init beamsearch
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):
+
+ if self.beam_search is None:
+
+
logging.info("enable beam_search")
self.init_beam_search(**kwargs)
self.nbest = kwargs.get("nbest", 1)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index 62d6be8..414c0d7 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -148,6 +148,7 @@
self._train_epoch(epoch)
+
if self.use_ddp or self.use_fsdp:
dist.barrier()
@@ -156,8 +157,8 @@
if self.use_ddp or self.use_fsdp:
dist.barrier()
-
-
+
+
if self.rank == 0:
self._save_checkpoint(epoch)
@@ -172,7 +173,8 @@
if self.use_ddp or self.use_fsdp:
dist.barrier()
-
+
+
if self.writer:
self.writer.close()
--
Gitblit v1.9.1