mirror of
https://github.com/modelscope/FunASR
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Funasr1.0 (#1277)
* funasr1.0 funetine * funasr1.0 pbar * update with main (#1260) * Update websocket_protocol_zh.md * update --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com> * update with main (#1264) * Funasr1.0 (#1261) * funasr1.0 funetine * funasr1.0 pbar * update with main (#1260) * Update websocket_protocol_zh.md * update --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com> --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com> * bug fix --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com> * funasr1.0 sanm scama * funasr1.0 infer_after_finetune * funasr1.0 fsmn-vad bug fix * funasr1.0 fsmn-vad bug fix * funasr1.0 fsmn-vad bug fix * funasr1.0 finetune * funasr1.0 finetune * funasr1.0 finetune * funasr1.0 finetune --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com>
This commit is contained in:
zhifu gao
GitHub
parent
2cca8104d2
commit
37d7764ecf
@ -132,7 +132,8 @@ class AutoModel:
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self.punc_kwargs = punc_kwargs
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self.spk_model = spk_model
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self.spk_kwargs = spk_kwargs
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self.model_path = kwargs.get("model_path", "./")
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self.model_path = kwargs.get("model_path")
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def build_model(self, **kwargs):
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@ -58,7 +58,7 @@ class AudioDataset(torch.utils.data.Dataset):
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data_src = load_audio_text_image_video(source, fs=self.fs)
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if self.preprocessor_speech:
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data_src = self.preprocessor_speech(data_src)
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speech, speech_lengths = extract_fbank(data_src, data_type=self.data_type, frontend=self.frontend) # speech: [b, T, d]
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speech, speech_lengths = extract_fbank(data_src, data_type=self.data_type, frontend=self.frontend, is_final=True) # speech: [b, T, d]
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target = item["target"]
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if self.preprocessor_text:
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@ -399,9 +399,10 @@ class WavFrontendOnline(nn.Module):
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return feats_pad, feats_lens, lfr_splice_frame_idxs
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def forward(
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self, input: torch.Tensor, input_lengths: torch.Tensor, cache: dict = {}, **kwargs
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self, input: torch.Tensor, input_lengths: torch.Tensor, **kwargs
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):
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is_final = kwargs.get("is_final", False)
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cache = kwargs.get("cache", {})
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if len(cache) == 0:
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self.init_cache(cache)
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@ -15,7 +15,7 @@ from funasr.register import tables
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from typing import List, Tuple, Dict, Any, Optional
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.utils.load_utils import load_audio_text_image_video,extract_fbank
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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class VadStateMachine(Enum):
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@ -23,11 +23,13 @@ class VadStateMachine(Enum):
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kVadInStateInSpeechSegment = 2
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kVadInStateEndPointDetected = 3
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class FrameState(Enum):
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kFrameStateInvalid = -1
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kFrameStateSpeech = 1
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kFrameStateSil = 0
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# final voice/unvoice state per frame
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class AudioChangeState(Enum):
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kChangeStateSpeech2Speech = 0
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@ -37,16 +39,19 @@ class AudioChangeState(Enum):
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kChangeStateNoBegin = 4
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kChangeStateInvalid = 5
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class VadDetectMode(Enum):
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kVadSingleUtteranceDetectMode = 0
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kVadMutipleUtteranceDetectMode = 1
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class VADXOptions:
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Deep-FSMN for Large Vocabulary Continuous Speech Recognition
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https://arxiv.org/abs/1803.05030
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"""
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def __init__(
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self,
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sample_rate: int = 16000,
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@ -117,6 +122,7 @@ class E2EVadSpeechBufWithDoa(object):
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Deep-FSMN for Large Vocabulary Continuous Speech Recognition
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https://arxiv.org/abs/1803.05030
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"""
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def __init__(self):
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self.start_ms = 0
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self.end_ms = 0
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@ -140,6 +146,7 @@ class E2EVadFrameProb(object):
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Deep-FSMN for Large Vocabulary Continuous Speech Recognition
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https://arxiv.org/abs/1803.05030
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"""
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def __init__(self):
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self.noise_prob = 0.0
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self.speech_prob = 0.0
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@ -154,6 +161,7 @@ class WindowDetector(object):
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Deep-FSMN for Large Vocabulary Continuous Speech Recognition
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https://arxiv.org/abs/1803.05030
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"""
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def __init__(self, window_size_ms: int,
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sil_to_speech_time: int,
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speech_to_sil_time: int,
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@ -190,7 +198,7 @@ class WindowDetector(object):
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def GetWinSize(self) -> int:
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return int(self.win_size_frame)
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def DetectOneFrame(self, frameState: FrameState, frame_count: int, cache: dict={}) -> AudioChangeState:
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def DetectOneFrame(self, frameState: FrameState, frame_count: int, cache: dict = {}) -> AudioChangeState:
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cur_frame_state = FrameState.kFrameStateSil
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if frameState == FrameState.kFrameStateSpeech:
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cur_frame_state = 1
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@ -220,13 +228,13 @@ class WindowDetector(object):
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def FrameSizeMs(self) -> int:
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return int(self.frame_size_ms)
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class Stats(object):
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def __init__(self,
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sil_pdf_ids,
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max_end_sil_frame_cnt_thresh,
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speech_noise_thres,
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):
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self.data_buf_start_frame = 0
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self.frm_cnt = 0
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self.latest_confirmed_speech_frame = 0
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@ -255,6 +263,7 @@ class Stats(object):
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self.waveform = None
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self.last_drop_frames = 0
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@tables.register("model_classes", "FsmnVADStreaming")
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class FsmnVADStreaming(nn.Module):
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"""
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@ -262,6 +271,7 @@ class FsmnVADStreaming(nn.Module):
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Deep-FSMN for Large Vocabulary Continuous Speech Recognition
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https://arxiv.org/abs/1803.05030
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"""
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def __init__(self,
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encoder: str = None,
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encoder_conf: Optional[Dict] = None,
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@ -275,7 +285,6 @@ class FsmnVADStreaming(nn.Module):
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encoder = encoder_class(**encoder_conf)
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self.encoder = encoder
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def ResetDetection(self, cache: dict = {}):
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cache["stats"].continous_silence_frame_count = 0
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cache["stats"].latest_confirmed_speech_frame = 0
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@ -292,7 +301,8 @@ class FsmnVADStreaming(nn.Module):
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drop_frames = int(cache["stats"].output_data_buf[-1].end_ms / self.vad_opts.frame_in_ms)
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real_drop_frames = drop_frames - cache["stats"].last_drop_frames
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cache["stats"].last_drop_frames = drop_frames
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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):]
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cache["stats"].data_buf_all = cache["stats"].data_buf_all[real_drop_frames * int(
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self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
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cache["stats"].decibel = cache["stats"].decibel[real_drop_frames:]
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cache["stats"].scores = cache["stats"].scores[:, real_drop_frames:, :]
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@ -300,7 +310,8 @@ class FsmnVADStreaming(nn.Module):
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frame_sample_length = int(self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000)
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frame_shift_length = int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000)
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if cache["stats"].data_buf_all is None:
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cache["stats"].data_buf_all = cache["stats"].waveform[0] # cache["stats"].data_buf is pointed to cache["stats"].waveform[0]
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cache["stats"].data_buf_all = cache["stats"].waveform[
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0] # cache["stats"].data_buf is pointed to cache["stats"].waveform[0]
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cache["stats"].data_buf = cache["stats"].data_buf_all
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else:
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cache["stats"].data_buf_all = torch.cat((cache["stats"].data_buf_all, cache["stats"].waveform[0]))
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@ -319,15 +330,16 @@ class FsmnVADStreaming(nn.Module):
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else:
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cache["stats"].scores = torch.cat((cache["stats"].scores, scores), dim=1)
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def PopDataBufTillFrame(self, frame_idx: int, cache: dict={}) -> None: # need check again
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def PopDataBufTillFrame(self, frame_idx: int, cache: dict = {}) -> None: # need check again
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while cache["stats"].data_buf_start_frame < frame_idx:
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if len(cache["stats"].data_buf) >= int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):
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cache["stats"].data_buf_start_frame += 1
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cache["stats"].data_buf = cache["stats"].data_buf_all[(cache["stats"].data_buf_start_frame - cache["stats"].last_drop_frames) * int(
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self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
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cache["stats"].data_buf = cache["stats"].data_buf_all[
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(cache["stats"].data_buf_start_frame - cache["stats"].last_drop_frames) * int(
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self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
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def PopDataToOutputBuf(self, start_frm: int, frm_cnt: int, first_frm_is_start_point: bool,
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last_frm_is_end_point: bool, end_point_is_sent_end: bool, cache: dict={}) -> None:
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last_frm_is_end_point: bool, end_point_is_sent_end: bool, cache: dict = {}) -> None:
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self.PopDataBufTillFrame(start_frm, cache=cache)
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expected_sample_number = int(frm_cnt * self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000)
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if last_frm_is_end_point:
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@ -379,14 +391,15 @@ class FsmnVADStreaming(nn.Module):
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cache["stats"].lastest_confirmed_silence_frame = valid_frame
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if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
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self.PopDataBufTillFrame(valid_frame, cache=cache)
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# silence_detected_callback_
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# pass
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def OnVoiceDetected(self, valid_frame: int, cache:dict={}) -> None:
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# silence_detected_callback_
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# pass
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def OnVoiceDetected(self, valid_frame: int, cache: dict = {}) -> None:
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cache["stats"].latest_confirmed_speech_frame = valid_frame
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self.PopDataToOutputBuf(valid_frame, 1, False, False, False, cache=cache)
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def OnVoiceStart(self, start_frame: int, fake_result: bool = False, cache:dict={}) -> None:
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def OnVoiceStart(self, start_frame: int, fake_result: bool = False, cache: dict = {}) -> None:
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if self.vad_opts.do_start_point_detection:
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pass
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if cache["stats"].confirmed_start_frame != -1:
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@ -397,7 +410,7 @@ class FsmnVADStreaming(nn.Module):
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if not fake_result and cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
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self.PopDataToOutputBuf(cache["stats"].confirmed_start_frame, 1, True, False, False, cache=cache)
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def OnVoiceEnd(self, end_frame: int, fake_result: bool, is_last_frame: bool, cache:dict={}) -> None:
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def OnVoiceEnd(self, end_frame: int, fake_result: bool, is_last_frame: bool, cache: dict = {}) -> None:
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for t in range(cache["stats"].latest_confirmed_speech_frame + 1, end_frame):
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self.OnVoiceDetected(t, cache=cache)
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if self.vad_opts.do_end_point_detection:
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@ -487,7 +500,8 @@ class FsmnVADStreaming(nn.Module):
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segment_batch = []
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if len(cache["stats"].output_data_buf) > 0:
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for i in range(cache["stats"].output_data_buf_offset, len(cache["stats"].output_data_buf)):
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if not is_final and (not cache["stats"].output_data_buf[i].contain_seg_start_point or not cache["stats"].output_data_buf[
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if not is_final and (not cache["stats"].output_data_buf[i].contain_seg_start_point or not
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cache["stats"].output_data_buf[
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i].contain_seg_end_point):
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continue
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segment = [cache["stats"].output_data_buf[i].start_ms, cache["stats"].output_data_buf[i].end_ms]
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@ -499,9 +513,9 @@ class FsmnVADStreaming(nn.Module):
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# # reset class variables and clear the dict for the next query
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# self.AllResetDetection()
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return segments
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def init_cache(self, cache: dict = {}, **kwargs):
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cache["frontend"] = {}
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cache["prev_samples"] = torch.empty(0)
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cache["encoder"] = {}
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@ -528,12 +542,12 @@ class FsmnVADStreaming(nn.Module):
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cache: dict = {},
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**kwargs,
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):
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if len(cache) == 0:
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self.init_cache(cache, **kwargs)
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meta_data = {}
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chunk_size = kwargs.get("chunk_size", 60000) # 50ms
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chunk_size = kwargs.get("chunk_size", 60000) # 50ms
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chunk_stride_samples = int(chunk_size * frontend.fs / 1000)
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time1 = time.perf_counter()
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@ -580,7 +594,6 @@ class FsmnVADStreaming(nn.Module):
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if len(segments_i) > 0:
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segments.extend(*segments_i)
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cache["prev_samples"] = audio_sample[:-m]
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if _is_final:
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self.init_cache(cache)
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@ -600,16 +613,15 @@ class FsmnVADStreaming(nn.Module):
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if ibest_writer is not None:
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ibest_writer["text"][key[0]] = segments
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return results, meta_data
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def DetectCommonFrames(self, cache: dict = {}) -> int:
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if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
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return 0
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for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1):
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frame_state = FrameState.kFrameStateInvalid
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frame_state = self.GetFrameState(cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames, cache=cache)
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frame_state = self.GetFrameState(cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames,
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cache=cache)
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self.DetectOneFrame(frame_state, cache["stats"].frm_cnt - 1 - i, False, cache=cache)
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return 0
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@ -619,7 +631,8 @@ class FsmnVADStreaming(nn.Module):
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return 0
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for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1):
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frame_state = FrameState.kFrameStateInvalid
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frame_state = self.GetFrameState(cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames, cache=cache)
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frame_state = self.GetFrameState(cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames,
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cache=cache)
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if i != 0:
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self.DetectOneFrame(frame_state, cache["stats"].frm_cnt - 1 - i, False, cache=cache)
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else:
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@ -627,7 +640,8 @@ class FsmnVADStreaming(nn.Module):
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return 0
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def DetectOneFrame(self, cur_frm_state: FrameState, cur_frm_idx: int, is_final_frame: bool, cache: dict = {}) -> None:
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def DetectOneFrame(self, cur_frm_state: FrameState, cur_frm_idx: int, is_final_frame: bool,
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cache: dict = {}) -> None:
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tmp_cur_frm_state = FrameState.kFrameStateInvalid
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if cur_frm_state == FrameState.kFrameStateSpeech:
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if math.fabs(1.0) > self.vad_opts.fe_prior_thres:
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@ -644,7 +658,8 @@ class FsmnVADStreaming(nn.Module):
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cache["stats"].pre_end_silence_detected = False
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start_frame = 0
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if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
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start_frame = max(cache["stats"].data_buf_start_frame, cur_frm_idx - self.LatencyFrmNumAtStartPoint(cache=cache))
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start_frame = max(cache["stats"].data_buf_start_frame,
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cur_frm_idx - self.LatencyFrmNumAtStartPoint(cache=cache))
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self.OnVoiceStart(start_frame, cache=cache)
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cache["stats"].vad_state_machine = VadStateMachine.kVadInStateInSpeechSegment
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for t in range(start_frame + 1, cur_frm_idx + 1):
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@ -696,7 +711,8 @@ class FsmnVADStreaming(nn.Module):
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if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
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# silence timeout, return zero length decision
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if ((self.vad_opts.detect_mode == VadDetectMode.kVadSingleUtteranceDetectMode.value) and (
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cache["stats"].continous_silence_frame_count * frm_shift_in_ms > self.vad_opts.max_start_silence_time)) \
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cache[
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"stats"].continous_silence_frame_count * frm_shift_in_ms > self.vad_opts.max_start_silence_time)) \
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or (is_final_frame and cache["stats"].number_end_time_detected == 0):
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for t in range(cache["stats"].lastest_confirmed_silence_frame + 1, cur_frm_idx):
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self.OnSilenceDetected(t, cache=cache)
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@ -707,7 +723,8 @@ class FsmnVADStreaming(nn.Module):
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if cur_frm_idx >= self.LatencyFrmNumAtStartPoint(cache=cache):
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self.OnSilenceDetected(cur_frm_idx - self.LatencyFrmNumAtStartPoint(cache=cache), cache=cache)
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elif cache["stats"].vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
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if cache["stats"].continous_silence_frame_count * frm_shift_in_ms >= cache["stats"].max_end_sil_frame_cnt_thresh:
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if cache["stats"].continous_silence_frame_count * frm_shift_in_ms >= cache[
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"stats"].max_end_sil_frame_cnt_thresh:
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lookback_frame = int(cache["stats"].max_end_sil_frame_cnt_thresh / frm_shift_in_ms)
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if self.vad_opts.do_extend:
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lookback_frame -= int(self.vad_opts.lookahead_time_end_point / frm_shift_in_ms)
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@ -733,4 +750,3 @@ class FsmnVADStreaming(nn.Module):
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self.ResetDetection(cache=cache)
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@ -11,7 +11,7 @@ from typing import Union
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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 @@ class BeamSearchScama(torch.nn.Module):
|
||||
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
|
||||
|
||||
@ -436,7 +436,10 @@ class SCAMA(nn.Module):
|
||||
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 @@ class SCAMA(nn.Module):
|
||||
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 @@ class SCAMA(nn.Module):
|
||||
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 @@ class SCAMA(nn.Module):
|
||||
|
||||
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 @@ class SCAMA(nn.Module):
|
||||
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 @@ class SCAMA(nn.Module):
|
||||
|
||||
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 @@ class SCAMA(nn.Module):
|
||||
"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 @@ class SCAMA(nn.Module):
|
||||
# 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)
|
||||
|
||||
@ -148,6 +148,7 @@ class Trainer:
|
||||
|
||||
self._train_epoch(epoch)
|
||||
|
||||
|
||||
|
||||
if self.use_ddp or self.use_fsdp:
|
||||
dist.barrier()
|
||||
@ -156,8 +157,8 @@ class Trainer:
|
||||
|
||||
if self.use_ddp or self.use_fsdp:
|
||||
dist.barrier()
|
||||
|
||||
|
||||
|
||||
|
||||
if self.rank == 0:
|
||||
self._save_checkpoint(epoch)
|
||||
|
||||
@ -172,7 +173,8 @@ class Trainer:
|
||||
|
||||
if self.use_ddp or self.use_fsdp:
|
||||
dist.barrier()
|
||||
|
||||
|
||||
|
||||
if self.writer:
|
||||
self.writer.close()
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user