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zhuyunfeng

2023-05-09 b15db52e4e67da8a133a67e8ffa415386de48b40

 funasr/bin/asr_inference_rnnt.py

@@ -16,13 +16,13 @@
from packaging.version import parse as V
from typeguard import check_argument_types, check_return_type

from funasr.models_transducer.beam_search_transducer import (
from funasr.modules.beam_search.beam_search_transducer import (
    BeamSearchTransducer,
    Hypothesis,
)
from funasr.models_transducer.utils import TooShortUttError
from funasr.modules.nets_utils import TooShortUttError
from funasr.fileio.datadir_writer import DatadirWriter
from funasr.tasks.asr_transducer import ASRTransducerTask
from funasr.tasks.asr import ASRTransducerTask
from funasr.tasks.lm import LMTask
from funasr.text.build_tokenizer import build_tokenizer
from funasr.text.token_id_converter import TokenIDConverter
@@ -174,7 +174,7 @@
        self.streaming = streaming
        self.simu_streaming = simu_streaming
        self.chunk_size = max(chunk_size, 0)
        self.left_context = max(left_context, 0)
        self.left_context = left_context
        self.right_context = max(right_context, 0)

        if not streaming or chunk_size == 0:
@@ -188,18 +188,15 @@
        self.frontend = frontend
        self.window_size = self.chunk_size + self.right_context
        
        self._ctx = self.asr_model.encoder.get_encoder_input_size(
            self.window_size
        )
        if self.streaming:
            self._ctx = self.asr_model.encoder.get_encoder_input_size(
                self.window_size
            )
       
        #self.last_chunk_length = (
        #    self.asr_model.encoder.embed.min_frame_length + self.right_context + 1
        #) * self.hop_length

        self.last_chunk_length = (
            self.asr_model.encoder.embed.min_frame_length + self.right_context + 1
        )
        self.reset_inference_cache()
            self.last_chunk_length = (
                self.asr_model.encoder.embed.min_frame_length + self.right_context + 1
            )
            self.reset_inference_cache()

    def reset_inference_cache(self) -> None:
        """Reset Speech2Text parameters."""
@@ -300,9 +297,6 @@
        
        feats = speech.unsqueeze(0).to(getattr(torch, self.dtype))
        feats_lengths = feats.new_full([1], dtype=torch.long, fill_value=feats.size(1))

        if self.asr_model.normalize is not None:
            feats, feats_lengths = self.asr_model.normalize(feats, feats_lengths)

        feats = to_device(feats, device=self.device)
        feats_lengths = to_device(feats_lengths, device=self.device)