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https://github.com/modelscope/FunASR
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Merge branch 'dev_gzf_funasr2' of github.com:alibaba-damo-academy/FunASR into dev_gzf_funasr2
add
This commit is contained in:
游雁
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@ -68,6 +68,8 @@ class DecoderLayerSANM(nn.Module):
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if self.concat_after:
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self.concat_linear1 = nn.Linear(size + size, size)
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self.concat_linear2 = nn.Linear(size + size, size)
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self.reserve_attn=False
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self.attn_mat = []
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def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
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"""Compute decoded features.
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@ -104,8 +106,13 @@ class DecoderLayerSANM(nn.Module):
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residual = x
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if self.normalize_before:
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x = self.norm3(x)
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x = residual + self.dropout(self.src_attn(x, memory, memory_mask))
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if self.reserve_attn:
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x_src_attn, attn_mat = self.src_attn(x, memory, memory_mask, ret_attn=True)
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self.attn_mat.append(attn_mat)
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else:
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x_src_attn = self.src_attn(x, memory, memory_mask, ret_attn=False)
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x = residual + self.dropout(x_src_attn)
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# x = residual + self.dropout(self.src_attn(x, memory, memory_mask))
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return x, tgt_mask, memory, memory_mask, cache
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@ -213,6 +220,7 @@ class ParaformerSANMDecoder(BaseTransformerDecoder):
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src_attention_dropout_rate: float = 0.0,
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input_layer: str = "embed",
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use_output_layer: bool = True,
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wo_input_layer: bool = False,
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pos_enc_class=PositionalEncoding,
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normalize_before: bool = True,
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concat_after: bool = False,
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@ -239,22 +247,24 @@ class ParaformerSANMDecoder(BaseTransformerDecoder):
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)
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attention_dim = encoder_output_size
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if input_layer == "embed":
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self.embed = torch.nn.Sequential(
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torch.nn.Embedding(vocab_size, attention_dim),
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# pos_enc_class(attention_dim, positional_dropout_rate),
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)
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elif input_layer == "linear":
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self.embed = torch.nn.Sequential(
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torch.nn.Linear(vocab_size, attention_dim),
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torch.nn.LayerNorm(attention_dim),
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torch.nn.Dropout(dropout_rate),
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torch.nn.ReLU(),
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pos_enc_class(attention_dim, positional_dropout_rate),
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)
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if wo_input_layer:
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self.embed = None
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else:
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raise ValueError(f"only 'embed' or 'linear' is supported: {input_layer}")
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if input_layer == "embed":
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self.embed = torch.nn.Sequential(
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torch.nn.Embedding(vocab_size, attention_dim),
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# pos_enc_class(attention_dim, positional_dropout_rate),
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)
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elif input_layer == "linear":
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self.embed = torch.nn.Sequential(
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torch.nn.Linear(vocab_size, attention_dim),
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torch.nn.LayerNorm(attention_dim),
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torch.nn.Dropout(dropout_rate),
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torch.nn.ReLU(),
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pos_enc_class(attention_dim, positional_dropout_rate),
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)
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else:
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raise ValueError(f"only 'embed' or 'linear' is supported: {input_layer}")
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self.normalize_before = normalize_before
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if self.normalize_before:
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@ -324,6 +334,8 @@ class ParaformerSANMDecoder(BaseTransformerDecoder):
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hlens: torch.Tensor,
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ys_in_pad: torch.Tensor,
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ys_in_lens: torch.Tensor,
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return_hidden: bool = False,
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return_both: bool= False,
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chunk_mask: torch.Tensor = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Forward decoder.
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@ -365,12 +377,16 @@ class ParaformerSANMDecoder(BaseTransformerDecoder):
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x, tgt_mask, memory, memory_mask
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)
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if self.normalize_before:
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x = self.after_norm(x)
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if self.output_layer is not None:
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x = self.output_layer(x)
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hidden = self.after_norm(x)
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olens = tgt_mask.sum(1)
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return x, olens
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if self.output_layer is not None and return_hidden is False:
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x = self.output_layer(hidden)
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return x, olens
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if return_both:
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x = self.output_layer(hidden)
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return x, hidden, olens
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return hidden, olens
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def score(self, ys, state, x):
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"""Score."""
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@ -449,7 +449,7 @@ class MultiHeadedAttentionCrossAtt(nn.Module):
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return q_h, k_h, v_h
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def forward_attention(self, value, scores, mask):
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def forward_attention(self, value, scores, mask, ret_attn=False):
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"""Compute attention context vector.
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Args:
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@ -476,16 +476,16 @@ class MultiHeadedAttentionCrossAtt(nn.Module):
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) # (batch, head, time1, time2)
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else:
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self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
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p_attn = self.dropout(self.attn)
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x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)
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x = (
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x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
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) # (batch, time1, d_model)
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if ret_attn:
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return self.linear_out(x), self.attn # (batch, time1, d_model)
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return self.linear_out(x) # (batch, time1, d_model)
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def forward(self, x, memory, memory_mask):
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def forward(self, x, memory, memory_mask, ret_attn=False):
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"""Compute scaled dot product attention.
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Args:
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@ -502,7 +502,7 @@ class MultiHeadedAttentionCrossAtt(nn.Module):
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q_h, k_h, v_h = self.forward_qkv(x, memory)
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q_h = q_h * self.d_k ** (-0.5)
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scores = torch.matmul(q_h, k_h.transpose(-2, -1))
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return self.forward_attention(v_h, scores, memory_mask)
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return self.forward_attention(v_h, scores, memory_mask, ret_attn=ret_attn)
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def forward_chunk(self, x, memory, cache=None, chunk_size=None, look_back=0):
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"""Compute scaled dot product attention.
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0
funasr/models/seaco_paraformer/__init__.py
Normal file
0
funasr/models/seaco_paraformer/__init__.py
Normal file
512
funasr/models/seaco_paraformer/model.py
Normal file
512
funasr/models/seaco_paraformer/model.py
Normal file
@ -0,0 +1,512 @@
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import os
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import logging
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from contextlib import contextmanager
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from distutils.version import LooseVersion
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from typing import Dict
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from typing import List
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from typing import Optional
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from typing import Tuple
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from typing import Union
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import tempfile
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import codecs
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import requests
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import re
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import copy
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import torch
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import torch.nn as nn
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import random
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import numpy as np
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import time
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# from funasr.layers.abs_normalize import AbsNormalize
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from funasr.losses.label_smoothing_loss import (
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LabelSmoothingLoss, # noqa: H301
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)
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# from funasr.models.ctc import CTC
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# from funasr.models.decoder.abs_decoder import AbsDecoder
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# from funasr.models.e2e_asr_common import ErrorCalculator
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# from funasr.models.encoder.abs_encoder import AbsEncoder
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# from funasr.frontends.abs_frontend import AbsFrontend
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# from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
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from funasr.models.paraformer.cif_predictor import mae_loss
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# from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
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# from funasr.models.specaug.abs_specaug import AbsSpecAug
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from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
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from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
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from funasr.metrics.compute_acc import th_accuracy
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from funasr.train_utils.device_funcs import force_gatherable
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# from funasr.models.base_model import FunASRModel
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# from funasr.models.paraformer.cif_predictor import CifPredictorV3
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from funasr.models.paraformer.search import Hypothesis
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if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
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from torch.cuda.amp import autocast
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else:
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# Nothing to do if torch<1.6.0
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@contextmanager
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def autocast(enabled=True):
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yield
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from funasr.datasets.audio_datasets.load_audio_extract_fbank import load_audio, extract_fbank
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from funasr.utils import postprocess_utils
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.models.paraformer.model import Paraformer
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from funasr.utils.register import register_class, registry_tables
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@register_class("model_classes", "SeacoParaformer")
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class SeacoParaformer(Paraformer):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability
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https://arxiv.org/abs/2308.03266
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"""
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def __init__(
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self,
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*args,
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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self.inner_dim = kwargs.get("inner_dim", 256)
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self.bias_encoder_type = kwargs.get("bias_encoder_type", "lstm")
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bias_encoder_dropout_rate = kwargs.get("bias_encoder_dropout_rate", 0.0)
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bias_encoder_bid = kwargs.get("bias_encoder_bid", False)
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seaco_lsm_weight = kwargs.get("seaco_lsm_weight", 0.0)
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seaco_length_normalized_loss = kwargs.get("seaco_length_normalized_loss", True)
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# bias encoder
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if self.bias_encoder_type == 'lstm':
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logging.warning("enable bias encoder sampling and contextual training")
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self.bias_encoder = torch.nn.LSTM(self.inner_dim,
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self.inner_dim,
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2,
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batch_first=True,
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dropout=bias_encoder_dropout_rate,
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bidirectional=bias_encoder_bid)
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if bias_encoder_bid:
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self.lstm_proj = torch.nn.Linear(self.inner_dim*2, self.inner_dim)
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else:
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self.lstm_proj = None
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self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
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elif self.bias_encoder_type == 'mean':
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logging.warning("enable bias encoder sampling and contextual training")
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self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
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else:
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logging.error("Unsupport bias encoder type: {}".format(self.bias_encoder_type))
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# seaco decoder
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seaco_decoder = kwargs.get("seaco_decoder", None)
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if seaco_decoder is not None:
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seaco_decoder_conf = kwargs.get("seaco_decoder_conf")
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seaco_decoder_class = registry_tables.decoder_classes.get(seaco_decoder.lower())
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self.seaco_decoder = seaco_decoder_class(
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vocab_size=self.vocab_size,
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encoder_output_size=self.inner_dim,
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**seaco_decoder_conf,
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)
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self.hotword_output_layer = torch.nn.Linear(self.inner_dim, self.vocab_size)
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self.criterion_seaco = LabelSmoothingLoss(
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size=self.vocab_size,
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padding_idx=self.ignore_id,
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smoothing=seaco_lsm_weight,
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normalize_length=seaco_length_normalized_loss,
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)
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self.train_decoder = kwargs.get("train_decoder", False)
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self.NO_BIAS = kwargs.get("NO_BIAS", 8377)
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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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) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
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"""Frontend + 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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assert text_lengths.dim() == 1, text_lengths.shape
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# Check that batch_size is unified
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assert (
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speech.shape[0]
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== speech_lengths.shape[0]
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== text.shape[0]
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== text_lengths.shape[0]
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), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
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hotword_pad = kwargs.get("hotword_pad")
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hotword_lengths = kwargs.get("hotword_lengths")
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dha_pad = kwargs.get("dha_pad")
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batch_size = speech.shape[0]
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self.step_cur += 1
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# for data-parallel
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text = text[:, : text_lengths.max()]
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speech = speech[:, :speech_lengths.max()]
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# 1. Encoder
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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if self.predictor_bias == 1:
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_, ys_pad = add_sos_eos(text, self.sos, self.eos, self.ignore_id)
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ys_lengths = text_lengths + self.predictor_bias
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stats = dict()
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loss_seaco = self._calc_seaco_loss(encoder_out,
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encoder_out_lens,
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ys_pad,
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ys_lengths,
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hotword_pad,
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hotword_lengths,
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dha_pad,
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)
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if self.train_decoder:
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loss_att, acc_att = self._calc_att_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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loss = loss_seaco + loss_att
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stats["loss_att"] = torch.clone(loss_att.detach())
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stats["acc_att"] = acc_att
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else:
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loss = loss_seaco
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stats["loss_seaco"] = torch.clone(loss_seaco.detach())
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stats["loss"] = torch.clone(loss.detach())
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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 = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
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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 _merge(self, cif_attended, dec_attended):
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return cif_attended + dec_attended
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def _calc_seaco_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_lengths: torch.Tensor,
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hotword_pad: torch.Tensor,
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hotword_lengths: torch.Tensor,
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dha_pad: torch.Tensor,
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):
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# predictor forward
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encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
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encoder_out.device)
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pre_acoustic_embeds, _, _, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask,
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ignore_id=self.ignore_id)
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# decoder forward
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decoder_out, _ = self.decoder(encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_lengths, return_hidden=True)
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selected = self._hotword_representation(hotword_pad,
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hotword_lengths)
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contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
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num_hot_word = contextual_info.shape[1]
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_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
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# dha core
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cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, pre_acoustic_embeds, ys_lengths)
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dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_out, ys_lengths)
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merged = self._merge(cif_attended, dec_attended)
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dha_output = self.hotword_output_layer(merged[:, :-1]) # remove the last token in loss calculation
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loss_att = self.criterion_seaco(dha_output, dha_pad)
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return loss_att
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def _seaco_decode_with_ASF(self,
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encoder_out,
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encoder_out_lens,
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sematic_embeds,
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ys_pad_lens,
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hw_list,
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nfilter=50,
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seaco_weight=1.0):
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# decoder forward
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decoder_out, decoder_hidden, _ = self.decoder(encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, return_hidden=True, return_both=True)
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decoder_pred = torch.log_softmax(decoder_out, dim=-1)
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if hw_list is not None:
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hw_lengths = [len(i) for i in hw_list]
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hw_list_ = [torch.Tensor(i).long() for i in hw_list]
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hw_list_pad = pad_list(hw_list_, 0).to(encoder_out.device)
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selected = self._hotword_representation(hw_list_pad, torch.Tensor(hw_lengths).int().to(encoder_out.device))
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contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
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num_hot_word = contextual_info.shape[1]
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_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
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# ASF Core
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if nfilter > 0 and nfilter < num_hot_word:
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for dec in self.seaco_decoder.decoders:
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dec.reserve_attn = True
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# cif_attended, _ = self.decoder2(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
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dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
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# cif_filter = torch.topk(self.decoder2.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1], min(nfilter, num_hot_word-1))[1].tolist()
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hotword_scores = self.seaco_decoder.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1]
|
||||
# hotword_scores /= torch.sqrt(torch.tensor(hw_lengths)[:-1].float()).to(hotword_scores.device)
|
||||
dec_filter = torch.topk(hotword_scores, min(nfilter, num_hot_word-1))[1].tolist()
|
||||
add_filter = dec_filter
|
||||
add_filter.append(len(hw_list_pad)-1)
|
||||
# filter hotword embedding
|
||||
selected = selected[add_filter]
|
||||
# again
|
||||
contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
|
||||
num_hot_word = contextual_info.shape[1]
|
||||
_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
|
||||
for dec in self.seaco_decoder.decoders:
|
||||
dec.attn_mat = []
|
||||
dec.reserve_attn = False
|
||||
|
||||
# SeACo Core
|
||||
cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
|
||||
dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
|
||||
merged = self._merge(cif_attended, dec_attended)
|
||||
|
||||
dha_output = self.hotword_output_layer(merged) # remove the last token in loss calculation
|
||||
dha_pred = torch.log_softmax(dha_output, dim=-1)
|
||||
# import pdb; pdb.set_trace()
|
||||
def _merge_res(dec_output, dha_output):
|
||||
lmbd = torch.Tensor([seaco_weight] * dha_output.shape[0])
|
||||
dha_ids = dha_output.max(-1)[-1][0]
|
||||
dha_mask = (dha_ids == 8377).int().unsqueeze(-1)
|
||||
a = (1 - lmbd) / lmbd
|
||||
b = 1 / lmbd
|
||||
a, b = a.to(dec_output.device), b.to(dec_output.device)
|
||||
dha_mask = (dha_mask + a.reshape(-1, 1, 1)) / b.reshape(-1, 1, 1)
|
||||
# logits = dec_output * dha_mask + dha_output[:,:,:-1] * (1-dha_mask)
|
||||
logits = dec_output * dha_mask + dha_output[:,:,:] * (1-dha_mask)
|
||||
return logits
|
||||
merged_pred = _merge_res(decoder_pred, dha_pred)
|
||||
return merged_pred
|
||||
else:
|
||||
return decoder_pred
|
||||
|
||||
def _hotword_representation(self,
|
||||
hotword_pad,
|
||||
hotword_lengths):
|
||||
if self.bias_encoder_type != 'lstm':
|
||||
logging.error("Unsupported bias encoder type")
|
||||
hw_embed = self.decoder.embed(hotword_pad)
|
||||
hw_embed, (_, _) = self.bias_encoder(hw_embed)
|
||||
if self.lstm_proj is not None:
|
||||
hw_embed = self.lstm_proj(hw_embed)
|
||||
_ind = np.arange(0, hw_embed.shape[0]).tolist()
|
||||
selected = hw_embed[_ind, [i-1 for i in hotword_lengths.detach().cpu().tolist()]]
|
||||
return selected
|
||||
|
||||
def generate(self,
|
||||
data_in,
|
||||
data_lengths=None,
|
||||
key: list = None,
|
||||
tokenizer=None,
|
||||
frontend=None,
|
||||
**kwargs,
|
||||
):
|
||||
|
||||
# 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):
|
||||
logging.info("enable beam_search")
|
||||
self.init_beam_search(**kwargs)
|
||||
self.nbest = kwargs.get("nbest", 1)
|
||||
|
||||
meta_data = {}
|
||||
|
||||
# extract fbank feats
|
||||
time1 = time.perf_counter()
|
||||
audio_sample_list = load_audio(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
|
||||
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.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"])
|
||||
|
||||
# hotword
|
||||
self.hotword_list = self.generate_hotwords_list(kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend)
|
||||
|
||||
# Encoder
|
||||
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
||||
if isinstance(encoder_out, tuple):
|
||||
encoder_out = encoder_out[0]
|
||||
|
||||
# predictor
|
||||
predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
|
||||
pre_acoustic_embeds, pre_token_length, _, _ = predictor_outs[0], predictor_outs[1], \
|
||||
predictor_outs[2], predictor_outs[3]
|
||||
pre_token_length = pre_token_length.round().long()
|
||||
if torch.max(pre_token_length) < 1:
|
||||
return []
|
||||
|
||||
|
||||
decoder_out = self._seaco_decode_with_ASF(encoder_out, encoder_out_lens,
|
||||
pre_acoustic_embeds,
|
||||
pre_token_length,
|
||||
hw_list=self.hotword_list)
|
||||
# decoder_out, _ = decoder_outs[0], decoder_outs[1]
|
||||
|
||||
results = []
|
||||
b, n, d = decoder_out.size()
|
||||
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]
|
||||
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):
|
||||
ibest_writer = None
|
||||
if ibest_writer is None and kwargs.get("output_dir") is not None:
|
||||
writer = DatadirWriter(kwargs.get("output_dir"))
|
||||
ibest_writer = 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))
|
||||
|
||||
if tokenizer is not None:
|
||||
# Change integer-ids to tokens
|
||||
token = tokenizer.ids2tokens(token_int)
|
||||
text = tokenizer.tokens2text(token)
|
||||
|
||||
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
|
||||
result_i = {"key": key[i], "token": token, "text": text, "text_postprocessed": text_postprocessed}
|
||||
|
||||
if ibest_writer is not None:
|
||||
ibest_writer["token"][key[i]] = " ".join(token)
|
||||
ibest_writer["text"][key[i]] = text
|
||||
ibest_writer["text_postprocessed"][key[i]] = text_postprocessed
|
||||
else:
|
||||
result_i = {"key": key[i], "token_int": token_int}
|
||||
results.append(result_i)
|
||||
|
||||
return results, meta_data
|
||||
|
||||
|
||||
def generate_hotwords_list(self, hotword_list_or_file, tokenizer=None, frontend=None):
|
||||
def load_seg_dict(seg_dict_file):
|
||||
seg_dict = {}
|
||||
assert isinstance(seg_dict_file, str)
|
||||
with open(seg_dict_file, "r", encoding="utf8") as f:
|
||||
lines = f.readlines()
|
||||
for line in lines:
|
||||
s = line.strip().split()
|
||||
key = s[0]
|
||||
value = s[1:]
|
||||
seg_dict[key] = " ".join(value)
|
||||
return seg_dict
|
||||
|
||||
def seg_tokenize(txt, seg_dict):
|
||||
pattern = re.compile(r'^[\u4E00-\u9FA50-9]+$')
|
||||
out_txt = ""
|
||||
for word in txt:
|
||||
word = word.lower()
|
||||
if word in seg_dict:
|
||||
out_txt += seg_dict[word] + " "
|
||||
else:
|
||||
if pattern.match(word):
|
||||
for char in word:
|
||||
if char in seg_dict:
|
||||
out_txt += seg_dict[char] + " "
|
||||
else:
|
||||
out_txt += "<unk>" + " "
|
||||
else:
|
||||
out_txt += "<unk>" + " "
|
||||
return out_txt.strip().split()
|
||||
|
||||
seg_dict = None
|
||||
if frontend.cmvn_file is not None:
|
||||
model_dir = os.path.dirname(frontend.cmvn_file)
|
||||
seg_dict_file = os.path.join(model_dir, 'seg_dict')
|
||||
if os.path.exists(seg_dict_file):
|
||||
seg_dict = load_seg_dict(seg_dict_file)
|
||||
else:
|
||||
seg_dict = None
|
||||
# for None
|
||||
if hotword_list_or_file is None:
|
||||
hotword_list = None
|
||||
# for local txt inputs
|
||||
elif os.path.exists(hotword_list_or_file) and hotword_list_or_file.endswith('.txt'):
|
||||
logging.info("Attempting to parse hotwords from local txt...")
|
||||
hotword_list = []
|
||||
hotword_str_list = []
|
||||
with codecs.open(hotword_list_or_file, 'r') as fin:
|
||||
for line in fin.readlines():
|
||||
hw = line.strip()
|
||||
hw_list = hw.split()
|
||||
if seg_dict is not None:
|
||||
hw_list = seg_tokenize(hw_list, seg_dict)
|
||||
hotword_str_list.append(hw)
|
||||
hotword_list.append(tokenizer.tokens2ids(hw_list))
|
||||
hotword_list.append([self.sos])
|
||||
hotword_str_list.append('<s>')
|
||||
logging.info("Initialized hotword list from file: {}, hotword list: {}."
|
||||
.format(hotword_list_or_file, hotword_str_list))
|
||||
# for url, download and generate txt
|
||||
elif hotword_list_or_file.startswith('http'):
|
||||
logging.info("Attempting to parse hotwords from url...")
|
||||
work_dir = tempfile.TemporaryDirectory().name
|
||||
if not os.path.exists(work_dir):
|
||||
os.makedirs(work_dir)
|
||||
text_file_path = os.path.join(work_dir, os.path.basename(hotword_list_or_file))
|
||||
local_file = requests.get(hotword_list_or_file)
|
||||
open(text_file_path, "wb").write(local_file.content)
|
||||
hotword_list_or_file = text_file_path
|
||||
hotword_list = []
|
||||
hotword_str_list = []
|
||||
with codecs.open(hotword_list_or_file, 'r') as fin:
|
||||
for line in fin.readlines():
|
||||
hw = line.strip()
|
||||
hw_list = hw.split()
|
||||
if seg_dict is not None:
|
||||
hw_list = seg_tokenize(hw_list, seg_dict)
|
||||
hotword_str_list.append(hw)
|
||||
hotword_list.append(tokenizer.tokens2ids(hw_list))
|
||||
hotword_list.append([self.sos])
|
||||
hotword_str_list.append('<s>')
|
||||
logging.info("Initialized hotword list from file: {}, hotword list: {}."
|
||||
.format(hotword_list_or_file, hotword_str_list))
|
||||
# for text str input
|
||||
elif not hotword_list_or_file.endswith('.txt'):
|
||||
logging.info("Attempting to parse hotwords as str...")
|
||||
hotword_list = []
|
||||
hotword_str_list = []
|
||||
for hw in hotword_list_or_file.strip().split():
|
||||
hotword_str_list.append(hw)
|
||||
hw_list = hw.strip().split()
|
||||
if seg_dict is not None:
|
||||
hw_list = seg_tokenize(hw_list, seg_dict)
|
||||
hotword_list.append(tokenizer.tokens2ids(hw_list))
|
||||
hotword_list.append([self.sos])
|
||||
hotword_str_list.append('<s>')
|
||||
logging.info("Hotword list: {}.".format(hotword_str_list))
|
||||
else:
|
||||
hotword_list = None
|
||||
return hotword_list
|
||||
|
||||
151
funasr/models/seaco_paraformer/template.yaml
Normal file
151
funasr/models/seaco_paraformer/template.yaml
Normal file
@ -0,0 +1,151 @@
|
||||
# This is an example that demonstrates how to configure a model file.
|
||||
# You can modify the configuration according to your own requirements.
|
||||
|
||||
# to print the register_table:
|
||||
# from funasr.utils.register import registry_tables
|
||||
# registry_tables.print()
|
||||
|
||||
# network architecture
|
||||
model: SeacoParaformer
|
||||
model_conf:
|
||||
ctc_weight: 0.0
|
||||
lsm_weight: 0.1
|
||||
length_normalized_loss: true
|
||||
predictor_weight: 1.0
|
||||
predictor_bias: 1
|
||||
sampling_ratio: 0.75
|
||||
inner_dim: 512
|
||||
bias_encoder_type: lstm
|
||||
bias_encoder_bid: false
|
||||
seaco_lsm_weight: 0.1
|
||||
seaco_length_normal: true
|
||||
train_decoder: false
|
||||
NO_BIAS: 8377
|
||||
|
||||
# encoder
|
||||
encoder: SANMEncoder
|
||||
encoder_conf:
|
||||
output_size: 512
|
||||
attention_heads: 4
|
||||
linear_units: 2048
|
||||
num_blocks: 50
|
||||
dropout_rate: 0.1
|
||||
positional_dropout_rate: 0.1
|
||||
attention_dropout_rate: 0.1
|
||||
input_layer: pe
|
||||
pos_enc_class: SinusoidalPositionEncoder
|
||||
normalize_before: true
|
||||
kernel_size: 11
|
||||
sanm_shfit: 0
|
||||
selfattention_layer_type: sanm
|
||||
|
||||
# decoder
|
||||
decoder: ParaformerSANMDecoder
|
||||
decoder_conf:
|
||||
attention_heads: 4
|
||||
linear_units: 2048
|
||||
num_blocks: 16
|
||||
dropout_rate: 0.1
|
||||
positional_dropout_rate: 0.1
|
||||
self_attention_dropout_rate: 0.1
|
||||
src_attention_dropout_rate: 0.1
|
||||
att_layer_num: 16
|
||||
kernel_size: 11
|
||||
sanm_shfit: 0
|
||||
|
||||
# seaco decoder
|
||||
seaco_decoder: ParaformerSANMDecoder
|
||||
seaco_decoder_conf:
|
||||
attention_heads: 4
|
||||
linear_units: 1024
|
||||
num_blocks: 4
|
||||
dropout_rate: 0.1
|
||||
positional_dropout_rate: 0.1
|
||||
self_attention_dropout_rate: 0.1
|
||||
src_attention_dropout_rate: 0.1
|
||||
kernel_size: 21
|
||||
sanm_shfit: 0
|
||||
use_output_layer: false
|
||||
wo_input_layer: true
|
||||
|
||||
predictor: CifPredictorV2
|
||||
predictor_conf:
|
||||
idim: 512
|
||||
threshold: 1.0
|
||||
l_order: 1
|
||||
r_order: 1
|
||||
tail_threshold: 0.45
|
||||
|
||||
# frontend related
|
||||
frontend: WavFrontend
|
||||
frontend_conf:
|
||||
fs: 16000
|
||||
window: hamming
|
||||
n_mels: 80
|
||||
frame_length: 25
|
||||
frame_shift: 10
|
||||
lfr_m: 7
|
||||
lfr_n: 6
|
||||
dither: 0.0
|
||||
|
||||
specaug: SpecAugLFR
|
||||
specaug_conf:
|
||||
apply_time_warp: false
|
||||
time_warp_window: 5
|
||||
time_warp_mode: bicubic
|
||||
apply_freq_mask: true
|
||||
freq_mask_width_range:
|
||||
- 0
|
||||
- 30
|
||||
lfr_rate: 6
|
||||
num_freq_mask: 1
|
||||
apply_time_mask: true
|
||||
time_mask_width_range:
|
||||
- 0
|
||||
- 12
|
||||
num_time_mask: 1
|
||||
|
||||
train_conf:
|
||||
accum_grad: 1
|
||||
grad_clip: 5
|
||||
max_epoch: 150
|
||||
val_scheduler_criterion:
|
||||
- valid
|
||||
- acc
|
||||
best_model_criterion:
|
||||
- - valid
|
||||
- acc
|
||||
- max
|
||||
keep_nbest_models: 10
|
||||
log_interval: 50
|
||||
|
||||
optim: adam
|
||||
optim_conf:
|
||||
lr: 0.0005
|
||||
scheduler: warmuplr
|
||||
scheduler_conf:
|
||||
warmup_steps: 30000
|
||||
|
||||
dataset: AudioDataset
|
||||
dataset_conf:
|
||||
index_ds: IndexDSJsonl
|
||||
batch_sampler: DynamicBatchLocalShuffleSampler
|
||||
batch_type: example # example or length
|
||||
batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
|
||||
max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length,
|
||||
buffer_size: 500
|
||||
shuffle: True
|
||||
num_workers: 0
|
||||
|
||||
tokenizer: CharTokenizer
|
||||
tokenizer_conf:
|
||||
unk_symbol: <unk>
|
||||
split_with_space: true
|
||||
|
||||
|
||||
ctc_conf:
|
||||
dropout_rate: 0.0
|
||||
ctc_type: builtin
|
||||
reduce: true
|
||||
ignore_nan_grad: true
|
||||
normalize: null
|
||||
Loading…
Reference in New Issue
Block a user