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https://github.com/modelscope/FunASR
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inference
This commit is contained in:
游雁
parent
06839ef605
commit
b84a203d16
@ -484,226 +484,6 @@ class SANMEncoder(nn.Module):
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return xs_pad, ilens, None
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@tables.register("encoder_classes", "SANMTPEncoder")
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class SANMTPEncoder(nn.Module):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition
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https://arxiv.org/abs/2006.01713
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"""
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def __init__(
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self,
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input_size: int,
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output_size: int = 256,
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attention_heads: int = 4,
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linear_units: int = 2048,
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num_blocks: int = 6,
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tp_blocks: int = 0,
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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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attention_dropout_rate: float = 0.0,
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stochastic_depth_rate: float = 0.0,
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input_layer: Optional[str] = "conv2d",
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pos_enc_class=SinusoidalPositionEncoder,
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normalize_before: bool = True,
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concat_after: bool = False,
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positionwise_layer_type: str = "linear",
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positionwise_conv_kernel_size: int = 1,
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padding_idx: int = -1,
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kernel_size: int = 11,
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sanm_shfit: int = 0,
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selfattention_layer_type: str = "sanm",
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):
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super().__init__()
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self._output_size = output_size
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if input_layer == "linear":
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self.embed = torch.nn.Sequential(
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torch.nn.Linear(input_size, output_size),
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torch.nn.LayerNorm(output_size),
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torch.nn.Dropout(dropout_rate),
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torch.nn.ReLU(),
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eval(pos_enc_class)(output_size, positional_dropout_rate),
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)
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elif input_layer == "linear_no_pos":
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self.embed = torch.nn.Sequential(
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torch.nn.Linear(input_size, output_size),
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torch.nn.LayerNorm(output_size),
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torch.nn.Dropout(dropout_rate),
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eval(pos_enc_class)(output_size, positional_dropout_rate, use_pos=False),
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)
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elif input_layer == "conv2d":
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self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
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elif input_layer == "conv2d2":
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self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
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elif input_layer == "conv2d6":
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self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate)
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elif input_layer == "conv2d8":
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self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate)
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elif input_layer == "embed":
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self.embed = torch.nn.Sequential(
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torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
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eval(pos_enc_class)(output_size, positional_dropout_rate),
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)
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elif input_layer is None:
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if input_size == output_size:
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self.embed = None
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else:
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self.embed = torch.nn.Linear(input_size, output_size)
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elif input_layer == "pe":
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self.embed = SinusoidalPositionEncoder()
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elif input_layer == "pe_online":
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self.embed = StreamSinusoidalPositionEncoder()
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else:
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raise ValueError("unknown input_layer: " + input_layer)
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self.normalize_before = normalize_before
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if positionwise_layer_type == "linear":
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positionwise_layer = PositionwiseFeedForward
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positionwise_layer_args = (
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output_size,
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linear_units,
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dropout_rate,
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)
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elif positionwise_layer_type == "conv1d":
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positionwise_layer = MultiLayeredConv1d
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positionwise_layer_args = (
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output_size,
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linear_units,
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positionwise_conv_kernel_size,
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dropout_rate,
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)
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elif positionwise_layer_type == "conv1d-linear":
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positionwise_layer = Conv1dLinear
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positionwise_layer_args = (
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output_size,
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linear_units,
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positionwise_conv_kernel_size,
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dropout_rate,
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)
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else:
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raise NotImplementedError("Support only linear or conv1d.")
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if selfattention_layer_type == "selfattn":
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encoder_selfattn_layer = MultiHeadedAttention
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encoder_selfattn_layer_args = (
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attention_heads,
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output_size,
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attention_dropout_rate,
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)
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elif selfattention_layer_type == "sanm":
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encoder_selfattn_layer = MultiHeadedAttentionSANM
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encoder_selfattn_layer_args0 = (
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attention_heads,
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input_size,
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output_size,
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attention_dropout_rate,
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kernel_size,
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sanm_shfit,
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)
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encoder_selfattn_layer_args = (
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attention_heads,
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output_size,
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output_size,
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attention_dropout_rate,
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kernel_size,
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sanm_shfit,
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)
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self.encoders0 = repeat(
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1,
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lambda lnum: EncoderLayerSANM(
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input_size,
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output_size,
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encoder_selfattn_layer(*encoder_selfattn_layer_args0),
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positionwise_layer(*positionwise_layer_args),
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dropout_rate,
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normalize_before,
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concat_after,
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),
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)
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self.encoders = repeat(
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num_blocks - 1,
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lambda lnum: EncoderLayerSANM(
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output_size,
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output_size,
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encoder_selfattn_layer(*encoder_selfattn_layer_args),
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positionwise_layer(*positionwise_layer_args),
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dropout_rate,
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normalize_before,
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concat_after,
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stochastic_depth_rate,
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),
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)
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self.tp_encoders = repeat(
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tp_blocks,
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lambda lnum: EncoderLayerSANM(
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output_size,
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output_size,
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encoder_selfattn_layer(*encoder_selfattn_layer_args),
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positionwise_layer(*positionwise_layer_args),
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dropout_rate,
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normalize_before,
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concat_after,
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stochastic_depth_rate,
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),
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)
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if self.normalize_before:
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self.after_norm = LayerNorm(output_size)
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self.tp_blocks = tp_blocks
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if self.tp_blocks > 0:
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self.tp_norm = LayerNorm(output_size)
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def output_size(self) -> int:
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return self._output_size
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def forward(
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self,
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xs_pad: torch.Tensor,
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ilens: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
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"""Embed positions in tensor.
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Args:
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xs_pad: input tensor (B, L, D)
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ilens: input length (B)
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prev_states: Not to be used now.
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Returns:
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position embedded tensor and mask
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"""
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masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
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xs_pad *= self.output_size() ** 0.5
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if self.embed is None:
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xs_pad = xs_pad
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elif (
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isinstance(self.embed, Conv2dSubsampling)
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or isinstance(self.embed, Conv2dSubsampling2)
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or isinstance(self.embed, Conv2dSubsampling6)
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or isinstance(self.embed, Conv2dSubsampling8)
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):
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short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
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if short_status:
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raise TooShortUttError(
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f"has {xs_pad.size(1)} frames and is too short for subsampling "
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+ f"(it needs more than {limit_size} frames), return empty results",
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xs_pad.size(1),
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limit_size,
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)
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xs_pad, masks = self.embed(xs_pad, masks)
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else:
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xs_pad = self.embed(xs_pad)
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# forward encoder1
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mask_shfit_chunk, mask_att_chunk_encoder = None, None
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encoder_outs = self.encoders0(xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder)
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xs_pad, masks = encoder_outs[0], encoder_outs[1]
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encoder_outs = self.encoders(xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder)
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xs_pad, masks = encoder_outs[0], encoder_outs[1]
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if self.normalize_before:
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xs_pad = self.after_norm(xs_pad)
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# forward encoder2
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olens = masks.squeeze(1).sum(1)
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mask_shfit_chunk2, mask_att_chunk_encoder2 = None, None
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for layer_idx, encoder_layer in enumerate(self.tp_encoders):
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encoder_outs = encoder_layer(xs_pad, masks, None, mask_shfit_chunk2, mask_att_chunk_encoder2)
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xs_pad, masks = encoder_outs[0], encoder_outs[1]
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if self.tp_blocks > 0:
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xs_pad = self.tp_norm(xs_pad)
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return xs_pad, olens
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class EncoderLayerSANMExport(nn.Module):
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def __init__(
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self,
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@ -1392,323 +1392,3 @@ class SenseVoiceSANM(nn.Module):
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from funasr.models.paraformer.search import Hypothesis
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from funasr.utils import postprocess_utils
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@tables.register("model_classes", "SenseVoiceSANMCTC")
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class SenseVoiceSANMCTC(nn.Module):
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"""CTC-attention hybrid Encoder-Decoder model"""
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def __init__(
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self,
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specaug: str = None,
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specaug_conf: dict = None,
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normalize: str = None,
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normalize_conf: dict = None,
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encoder: str = None,
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encoder_conf: dict = None,
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ctc_conf: dict = None,
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input_size: int = 80,
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vocab_size: int = -1,
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ignore_id: int = -1,
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blank_id: int = 0,
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sos: int = 1,
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eos: int = 2,
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length_normalized_loss: bool = False,
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**kwargs,
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):
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super().__init__()
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if specaug is not None:
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specaug_class = tables.specaug_classes.get(specaug)
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specaug = specaug_class(**specaug_conf)
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if normalize is not None:
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normalize_class = tables.normalize_classes.get(normalize)
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normalize = normalize_class(**normalize_conf)
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encoder_class = tables.encoder_classes.get(encoder)
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encoder = encoder_class(input_size=input_size, **encoder_conf)
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encoder_output_size = encoder.output_size()
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if ctc_conf is None:
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ctc_conf = {}
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ctc = CTC(odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf)
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self.blank_id = blank_id
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self.sos = sos if sos is not None else vocab_size - 1
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self.eos = eos if eos is not None else vocab_size - 1
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self.vocab_size = vocab_size
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self.ignore_id = ignore_id
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self.specaug = specaug
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self.normalize = normalize
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self.encoder = encoder
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self.length_normalized_loss = length_normalized_loss
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self.error_calculator = None
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self.criterion_att = LabelSmoothingLoss(
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size=self.vocab_size,
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padding_idx=self.ignore_id,
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smoothing=kwargs.get("lsm_weight", 0.0),
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normalize_length=self.length_normalized_loss,
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)
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self.ctc = ctc
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self.length_normalized_loss = length_normalized_loss
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self.encoder_output_size = encoder_output_size
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self.lid_dict = {"auto": 0, "zh": 3, "en": 4, "yue": 7, "ja": 11, "ko": 12, "nospeech": 13}
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self.lid_int_dict = {24884: 3, 24885: 4, 24888: 7, 24892: 11, 24896: 12, 24992: 13}
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self.textnorm_dict = {"withitn": 14, "woitn": 15}
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self.textnorm_int_dict = {25016: 14, 25017: 15}
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self.embed = torch.nn.Embedding(7 + len(self.lid_dict) + len(self.textnorm_dict), input_size)
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def forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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text: torch.Tensor,
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text_lengths: torch.Tensor,
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**kwargs,
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):
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"""Encoder + Decoder + Calc loss
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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text: (Batch, Length)
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text_lengths: (Batch,)
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"""
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# import pdb;
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# pdb.set_trace()
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if len(text_lengths.size()) > 1:
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text_lengths = text_lengths[:, 0]
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if len(speech_lengths.size()) > 1:
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speech_lengths = speech_lengths[:, 0]
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batch_size = speech.shape[0]
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# 1. Encoder
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, text)
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loss_ctc, cer_ctc = None, None
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loss_rich, acc_rich = None, None
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stats = dict()
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loss_ctc, cer_ctc = self._calc_ctc_loss(
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encoder_out[:, 4:, :], encoder_out_lens - 4, text[:, 4:], text_lengths - 4
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)
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loss_rich, acc_rich = self._calc_rich_ce_loss(
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encoder_out[:, :4, :], text[:, :4]
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)
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loss = loss_ctc
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# Collect total loss stats
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stats["loss"] = torch.clone(loss.detach()) if loss_ctc is not None else None
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stats["loss_rich"] = torch.clone(loss_rich.detach()) if loss_rich is not None else None
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stats["acc_rich"] = acc_rich
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# force_gatherable: to-device and to-tensor if scalar for DataParallel
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if self.length_normalized_loss:
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batch_size = int((text_lengths + 1).sum())
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loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
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return loss, stats, weight
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def encode(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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text: torch.Tensor,
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**kwargs,
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):
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"""Frontend + Encoder. Note that this method is used by asr_inference.py
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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ind: int
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"""
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# Data augmentation
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if self.specaug is not None and self.training:
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speech, speech_lengths = self.specaug(speech, speech_lengths)
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# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
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if self.normalize is not None:
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speech, speech_lengths = self.normalize(speech, speech_lengths)
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lids = torch.LongTensor([[self.lid_int_dict[int(lid)] if torch.rand(1) > 0.2 and int(lid) in self.lid_int_dict else 0 ] for lid in text[:, 0]]).to(speech.device)
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language_query = self.embed(lids)
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styles = torch.LongTensor([[self.textnorm_int_dict[int(style)]] for style in text[:, 3]]).to(speech.device)
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style_query = self.embed(styles)
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speech = torch.cat((style_query, speech), dim=1)
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speech_lengths += 1
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event_emo_query = self.embed(torch.LongTensor([[1, 2]]).to(speech.device)).repeat(speech.size(0), 1, 1)
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input_query = torch.cat((language_query, event_emo_query), dim=1)
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speech = torch.cat((input_query, speech), dim=1)
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speech_lengths += 3
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# Forward encoder
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# feats: (Batch, Length, Dim)
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# -> encoder_out: (Batch, Length2, Dim2)
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encoder_out, encoder_out_lens = self.encoder(speech, speech_lengths)
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return encoder_out, encoder_out_lens
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def _calc_ctc_loss(
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self,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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ys_pad: torch.Tensor,
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ys_pad_lens: torch.Tensor,
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):
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# Calc CTC loss
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loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
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# Calc CER using CTC
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cer_ctc = None
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if not self.training and self.error_calculator is not None:
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ys_hat = self.ctc.argmax(encoder_out).data
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cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
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return loss_ctc, cer_ctc
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def _calc_rich_ce_loss(
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self,
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encoder_out: torch.Tensor,
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ys_pad: torch.Tensor,
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):
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decoder_out = self.ctc.ctc_lo(encoder_out)
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# 2. Compute attention loss
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loss_rich = self.criterion_att(decoder_out, ys_pad.contiguous())
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acc_rich = th_accuracy(
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decoder_out.view(-1, self.vocab_size),
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ys_pad.contiguous(),
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ignore_label=self.ignore_id,
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)
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return loss_rich, acc_rich
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def inference(
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self,
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data_in,
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data_lengths=None,
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key: list = None,
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tokenizer=None,
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frontend=None,
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**kwargs,
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):
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if kwargs.get("batch_size", 1) > 1:
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raise NotImplementedError("batch decoding is not implemented")
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meta_data = {}
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if (
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isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
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): # fbank
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speech, speech_lengths = data_in, data_lengths
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if len(speech.shape) < 3:
|
||||
speech = speech[None, :, :]
|
||||
if speech_lengths is None:
|
||||
speech_lengths = speech.shape[1]
|
||||
else:
|
||||
# extract fbank feats
|
||||
time1 = time.perf_counter()
|
||||
audio_sample_list = load_audio_text_image_video(
|
||||
data_in,
|
||||
fs=frontend.fs,
|
||||
audio_fs=kwargs.get("fs", 16000),
|
||||
data_type=kwargs.get("data_type", "sound"),
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
time2 = time.perf_counter()
|
||||
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
||||
speech, speech_lengths = extract_fbank(
|
||||
audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
|
||||
)
|
||||
time3 = time.perf_counter()
|
||||
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
||||
meta_data["batch_data_time"] = (
|
||||
speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
|
||||
)
|
||||
|
||||
speech = speech.to(device=kwargs["device"])
|
||||
speech_lengths = speech_lengths.to(device=kwargs["device"])
|
||||
|
||||
language = kwargs.get("language", None)
|
||||
if language is not None:
|
||||
language_query = self.embed(
|
||||
torch.LongTensor(
|
||||
[[self.lid_dict[language] if language in self.lid_dict else 0]]
|
||||
).to(speech.device)
|
||||
).repeat(speech.size(0), 1, 1)
|
||||
else:
|
||||
language_query = self.embed(torch.LongTensor([[0]]).to(speech.device)).repeat(
|
||||
speech.size(0), 1, 1
|
||||
)
|
||||
textnorm = kwargs.get("text_norm", "woitn")
|
||||
textnorm_query = self.embed(
|
||||
torch.LongTensor([[self.textnorm_dict[textnorm]]]).to(speech.device)
|
||||
).repeat(speech.size(0), 1, 1)
|
||||
speech = torch.cat((textnorm_query, speech), dim=1)
|
||||
speech_lengths += 1
|
||||
|
||||
event_emo_query = self.embed(torch.LongTensor([[1, 2]]).to(speech.device)).repeat(
|
||||
speech.size(0), 1, 1
|
||||
)
|
||||
input_query = torch.cat((language_query, event_emo_query), dim=1)
|
||||
speech = torch.cat((input_query, speech), dim=1)
|
||||
speech_lengths += 3
|
||||
|
||||
# Encoder
|
||||
encoder_out, encoder_out_lens = self.encoder(speech, speech_lengths)
|
||||
if isinstance(encoder_out, tuple):
|
||||
encoder_out = encoder_out[0]
|
||||
|
||||
# c. Passed the encoder result and the beam search
|
||||
ctc_logits = self.ctc.log_softmax(encoder_out)
|
||||
|
||||
results = []
|
||||
b, n, d = encoder_out.size()
|
||||
if isinstance(key[0], (list, tuple)):
|
||||
key = key[0]
|
||||
if len(key) < b:
|
||||
key = key * b
|
||||
for i in range(b):
|
||||
x = ctc_logits[i, : encoder_out_lens[i], :]
|
||||
yseq = x.argmax(dim=-1)
|
||||
yseq = torch.unique_consecutive(yseq, dim=-1)
|
||||
yseq = torch.tensor([self.sos] + yseq.tolist() + [self.eos], device=yseq.device)
|
||||
nbest_hyps = [Hypothesis(yseq=yseq)]
|
||||
|
||||
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
|
||||
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_postprocessed
|
||||
|
||||
return results, meta_data
|
||||
|
||||
@ -49,3 +49,9 @@ class SentencepiecesTokenizer(BaseTokenizer):
|
||||
|
||||
def get_vocab_size(self):
|
||||
return self.sp.GetPieceSize()
|
||||
|
||||
def ids2tokens(self, *args, **kwargs):
|
||||
return self.decode(*args, **kwargs)
|
||||
|
||||
def tokens2ids(self, *args, **kwargs):
|
||||
return self.encode(*args, **kwargs)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user