From e772c7eb9e5439aaff2f599e79f0b3c8fdca22c2 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 21 二月 2024 16:55:02 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR merge
---
/dev/null | 701 -----------------------------
funasr/models/bat/model.py | 706 +++++++++++++++-------------
2 files changed, 373 insertions(+), 1,034 deletions(-)
diff --git a/funasr/models/bat/attention.py b/funasr/models/bat/attention.py
deleted file mode 100644
index 11645b3..0000000
--- a/funasr/models/bat/attention.py
+++ /dev/null
@@ -1,238 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Shigeki Karita
-# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Multi-Head Attention layer definition."""
-
-import math
-
-import numpy
-import torch
-from torch import nn
-from typing import Optional, Tuple
-
-import torch.nn.functional as F
-from funasr.models.transformer.utils.nets_utils import make_pad_mask
-import funasr.models.lora.layers as lora
-
-
-class RelPositionMultiHeadedAttentionChunk(torch.nn.Module):
- """RelPositionMultiHeadedAttention definition.
- Args:
- num_heads: Number of attention heads.
- embed_size: Embedding size.
- dropout_rate: Dropout rate.
- """
-
- def __init__(
- self,
- num_heads: int,
- embed_size: int,
- dropout_rate: float = 0.0,
- simplified_attention_score: bool = False,
- ) -> None:
- """Construct an MultiHeadedAttention object."""
- super().__init__()
-
- self.d_k = embed_size // num_heads
- self.num_heads = num_heads
-
- assert self.d_k * num_heads == embed_size, (
- "embed_size (%d) must be divisible by num_heads (%d)",
- (embed_size, num_heads),
- )
-
- self.linear_q = torch.nn.Linear(embed_size, embed_size)
- self.linear_k = torch.nn.Linear(embed_size, embed_size)
- self.linear_v = torch.nn.Linear(embed_size, embed_size)
-
- self.linear_out = torch.nn.Linear(embed_size, embed_size)
-
- if simplified_attention_score:
- self.linear_pos = torch.nn.Linear(embed_size, num_heads)
-
- self.compute_att_score = self.compute_simplified_attention_score
- else:
- self.linear_pos = torch.nn.Linear(embed_size, embed_size, bias=False)
-
- self.pos_bias_u = torch.nn.Parameter(torch.Tensor(num_heads, self.d_k))
- self.pos_bias_v = torch.nn.Parameter(torch.Tensor(num_heads, self.d_k))
- torch.nn.init.xavier_uniform_(self.pos_bias_u)
- torch.nn.init.xavier_uniform_(self.pos_bias_v)
-
- self.compute_att_score = self.compute_attention_score
-
- self.dropout = torch.nn.Dropout(p=dropout_rate)
- self.attn = None
-
- def rel_shift(self, x: torch.Tensor, left_context: int = 0) -> torch.Tensor:
- """Compute relative positional encoding.
- Args:
- x: Input sequence. (B, H, T_1, 2 * T_1 - 1)
- left_context: Number of frames in left context.
- Returns:
- x: Output sequence. (B, H, T_1, T_2)
- """
- batch_size, n_heads, time1, n = x.shape
- time2 = time1 + left_context
-
- batch_stride, n_heads_stride, time1_stride, n_stride = x.stride()
-
- return x.as_strided(
- (batch_size, n_heads, time1, time2),
- (batch_stride, n_heads_stride, time1_stride - n_stride, n_stride),
- storage_offset=(n_stride * (time1 - 1)),
- )
-
- def compute_simplified_attention_score(
- self,
- query: torch.Tensor,
- key: torch.Tensor,
- pos_enc: torch.Tensor,
- left_context: int = 0,
- ) -> torch.Tensor:
- """Simplified attention score computation.
- Reference: https://github.com/k2-fsa/icefall/pull/458
- Args:
- query: Transformed query tensor. (B, H, T_1, d_k)
- key: Transformed key tensor. (B, H, T_2, d_k)
- pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
- left_context: Number of frames in left context.
- Returns:
- : Attention score. (B, H, T_1, T_2)
- """
- pos_enc = self.linear_pos(pos_enc)
-
- matrix_ac = torch.matmul(query, key.transpose(2, 3))
-
- matrix_bd = self.rel_shift(
- pos_enc.transpose(1, 2).unsqueeze(2).repeat(1, 1, query.size(2), 1),
- left_context=left_context,
- )
-
- return (matrix_ac + matrix_bd) / math.sqrt(self.d_k)
-
- def compute_attention_score(
- self,
- query: torch.Tensor,
- key: torch.Tensor,
- pos_enc: torch.Tensor,
- left_context: int = 0,
- ) -> torch.Tensor:
- """Attention score computation.
- Args:
- query: Transformed query tensor. (B, H, T_1, d_k)
- key: Transformed key tensor. (B, H, T_2, d_k)
- pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
- left_context: Number of frames in left context.
- Returns:
- : Attention score. (B, H, T_1, T_2)
- """
- p = self.linear_pos(pos_enc).view(pos_enc.size(0), -1, self.num_heads, self.d_k)
-
- query = query.transpose(1, 2)
- q_with_bias_u = (query + self.pos_bias_u).transpose(1, 2)
- q_with_bias_v = (query + self.pos_bias_v).transpose(1, 2)
-
- matrix_ac = torch.matmul(q_with_bias_u, key.transpose(-2, -1))
-
- matrix_bd = torch.matmul(q_with_bias_v, p.permute(0, 2, 3, 1))
- matrix_bd = self.rel_shift(matrix_bd, left_context=left_context)
-
- return (matrix_ac + matrix_bd) / math.sqrt(self.d_k)
-
- def forward_qkv(
- self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
- ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
- """Transform query, key and value.
- Args:
- query: Query tensor. (B, T_1, size)
- key: Key tensor. (B, T_2, size)
- v: Value tensor. (B, T_2, size)
- Returns:
- q: Transformed query tensor. (B, H, T_1, d_k)
- k: Transformed key tensor. (B, H, T_2, d_k)
- v: Transformed value tensor. (B, H, T_2, d_k)
- """
- n_batch = query.size(0)
-
- q = (
- self.linear_q(query)
- .view(n_batch, -1, self.num_heads, self.d_k)
- .transpose(1, 2)
- )
- k = (
- self.linear_k(key)
- .view(n_batch, -1, self.num_heads, self.d_k)
- .transpose(1, 2)
- )
- v = (
- self.linear_v(value)
- .view(n_batch, -1, self.num_heads, self.d_k)
- .transpose(1, 2)
- )
-
- return q, k, v
-
- def forward_attention(
- self,
- value: torch.Tensor,
- scores: torch.Tensor,
- mask: torch.Tensor,
- chunk_mask: Optional[torch.Tensor] = None,
- ) -> torch.Tensor:
- """Compute attention context vector.
- Args:
- value: Transformed value. (B, H, T_2, d_k)
- scores: Attention score. (B, H, T_1, T_2)
- mask: Source mask. (B, T_2)
- chunk_mask: Chunk mask. (T_1, T_1)
- Returns:
- attn_output: Transformed value weighted by attention score. (B, T_1, H * d_k)
- """
- batch_size = scores.size(0)
- mask = mask.unsqueeze(1).unsqueeze(2)
- if chunk_mask is not None:
- mask = chunk_mask.unsqueeze(0).unsqueeze(1) | mask
- scores = scores.masked_fill(mask, float("-inf"))
- self.attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0)
-
- attn_output = self.dropout(self.attn)
- attn_output = torch.matmul(attn_output, value)
-
- attn_output = self.linear_out(
- attn_output.transpose(1, 2)
- .contiguous()
- .view(batch_size, -1, self.num_heads * self.d_k)
- )
-
- return attn_output
-
- def forward(
- self,
- query: torch.Tensor,
- key: torch.Tensor,
- value: torch.Tensor,
- pos_enc: torch.Tensor,
- mask: torch.Tensor,
- chunk_mask: Optional[torch.Tensor] = None,
- left_context: int = 0,
- ) -> torch.Tensor:
- """Compute scaled dot product attention with rel. positional encoding.
- Args:
- query: Query tensor. (B, T_1, size)
- key: Key tensor. (B, T_2, size)
- value: Value tensor. (B, T_2, size)
- pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
- mask: Source mask. (B, T_2)
- chunk_mask: Chunk mask. (T_1, T_1)
- left_context: Number of frames in left context.
- Returns:
- : Output tensor. (B, T_1, H * d_k)
- """
- q, k, v = self.forward_qkv(query, key, value)
- scores = self.compute_att_score(q, k, pos_enc, left_context=left_context)
- return self.forward_attention(v, scores, mask, chunk_mask=chunk_mask)
-
diff --git a/funasr/models/bat/cif_predictor.py b/funasr/models/bat/cif_predictor.py
deleted file mode 100644
index d8915c2..0000000
--- a/funasr/models/bat/cif_predictor.py
+++ /dev/null
@@ -1,220 +0,0 @@
-# import torch
-# from torch import nn
-# from torch import Tensor
-# import logging
-# import numpy as np
-# from funasr.train_utils.device_funcs import to_device
-# from funasr.models.transformer.utils.nets_utils import make_pad_mask
-# from funasr.models.scama.utils import sequence_mask
-# from typing import Optional, Tuple
-#
-# from funasr.register import tables
-#
-# class mae_loss(nn.Module):
-#
-# def __init__(self, normalize_length=False):
-# super(mae_loss, self).__init__()
-# self.normalize_length = normalize_length
-# self.criterion = torch.nn.L1Loss(reduction='sum')
-#
-# def forward(self, token_length, pre_token_length):
-# loss_token_normalizer = token_length.size(0)
-# if self.normalize_length:
-# loss_token_normalizer = token_length.sum().type(torch.float32)
-# loss = self.criterion(token_length, pre_token_length)
-# loss = loss / loss_token_normalizer
-# return loss
-#
-#
-# def cif(hidden, alphas, threshold):
-# batch_size, len_time, hidden_size = hidden.size()
-#
-# # loop varss
-# integrate = torch.zeros([batch_size], device=hidden.device)
-# frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
-# # intermediate vars along time
-# list_fires = []
-# list_frames = []
-#
-# for t in range(len_time):
-# alpha = alphas[:, t]
-# distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
-#
-# integrate += alpha
-# list_fires.append(integrate)
-#
-# fire_place = integrate >= threshold
-# integrate = torch.where(fire_place,
-# integrate - torch.ones([batch_size], device=hidden.device),
-# integrate)
-# cur = torch.where(fire_place,
-# distribution_completion,
-# alpha)
-# remainds = alpha - cur
-#
-# frame += cur[:, None] * hidden[:, t, :]
-# list_frames.append(frame)
-# frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
-# remainds[:, None] * hidden[:, t, :],
-# frame)
-#
-# fires = torch.stack(list_fires, 1)
-# frames = torch.stack(list_frames, 1)
-# list_ls = []
-# len_labels = torch.round(alphas.sum(-1)).int()
-# max_label_len = len_labels.max()
-# for b in range(batch_size):
-# fire = fires[b, :]
-# l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
-# pad_l = torch.zeros([max_label_len - l.size(0), hidden_size], device=hidden.device)
-# list_ls.append(torch.cat([l, pad_l], 0))
-# return torch.stack(list_ls, 0), fires
-#
-#
-# def cif_wo_hidden(alphas, threshold):
-# batch_size, len_time = alphas.size()
-#
-# # loop varss
-# integrate = torch.zeros([batch_size], device=alphas.device)
-# # intermediate vars along time
-# list_fires = []
-#
-# for t in range(len_time):
-# alpha = alphas[:, t]
-#
-# integrate += alpha
-# list_fires.append(integrate)
-#
-# fire_place = integrate >= threshold
-# integrate = torch.where(fire_place,
-# integrate - torch.ones([batch_size], device=alphas.device)*threshold,
-# integrate)
-#
-# fires = torch.stack(list_fires, 1)
-# return fires
-#
-# @tables.register("predictor_classes", "BATPredictor")
-# class BATPredictor(nn.Module):
-# def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0, return_accum=False):
-# super(BATPredictor, self).__init__()
-#
-# self.pad = nn.ConstantPad1d((l_order, r_order), 0)
-# self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim)
-# self.cif_output = nn.Linear(idim, 1)
-# self.dropout = torch.nn.Dropout(p=dropout)
-# self.threshold = threshold
-# self.smooth_factor = smooth_factor
-# self.noise_threshold = noise_threshold
-# self.return_accum = return_accum
-#
-# def cif(
-# self,
-# input: Tensor,
-# alpha: Tensor,
-# beta: float = 1.0,
-# return_accum: bool = False,
-# ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
-# B, S, C = input.size()
-# assert tuple(alpha.size()) == (B, S), f"{alpha.size()} != {(B, S)}"
-#
-# dtype = alpha.dtype
-# alpha = alpha.float()
-#
-# alpha_sum = alpha.sum(1)
-# feat_lengths = (alpha_sum / beta).floor().long()
-# T = feat_lengths.max()
-#
-# # aggregate and integrate
-# csum = alpha.cumsum(-1)
-# with torch.no_grad():
-# # indices used for scattering
-# right_idx = (csum / beta).floor().long().clip(max=T)
-# left_idx = right_idx.roll(1, dims=1)
-# left_idx[:, 0] = 0
-#
-# # count # of fires from each source
-# fire_num = right_idx - left_idx
-# extra_weights = (fire_num - 1).clip(min=0)
-# # The extra entry in last dim is for
-# output = input.new_zeros((B, T + 1, C))
-# source_range = torch.arange(1, 1 + S).unsqueeze(0).type_as(input)
-# zero = alpha.new_zeros((1,))
-#
-# # right scatter
-# fire_mask = fire_num > 0
-# right_weight = torch.where(
-# fire_mask,
-# csum - right_idx.type_as(alpha) * beta,
-# zero
-# ).type_as(input)
-# # assert right_weight.ge(0).all(), f"{right_weight} should be non-negative."
-# output.scatter_add_(
-# 1,
-# right_idx.unsqueeze(-1).expand(-1, -1, C),
-# right_weight.unsqueeze(-1) * input
-# )
-#
-# # left scatter
-# left_weight = (
-# alpha - right_weight - extra_weights.type_as(alpha) * beta
-# ).type_as(input)
-# output.scatter_add_(
-# 1,
-# left_idx.unsqueeze(-1).expand(-1, -1, C),
-# left_weight.unsqueeze(-1) * input
-# )
-#
-# # extra scatters
-# if extra_weights.ge(0).any():
-# extra_steps = extra_weights.max().item()
-# tgt_idx = left_idx
-# src_feats = input * beta
-# for _ in range(extra_steps):
-# tgt_idx = (tgt_idx + 1).clip(max=T)
-# # (B, S, 1)
-# src_mask = (extra_weights > 0)
-# output.scatter_add_(
-# 1,
-# tgt_idx.unsqueeze(-1).expand(-1, -1, C),
-# src_feats * src_mask.unsqueeze(2)
-# )
-# extra_weights -= 1
-#
-# output = output[:, :T, :]
-#
-# if return_accum:
-# return output, csum
-# else:
-# return output, alpha
-#
-# def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None, target_label_length=None):
-# h = hidden
-# context = h.transpose(1, 2)
-# queries = self.pad(context)
-# memory = self.cif_conv1d(queries)
-# output = memory + context
-# output = self.dropout(output)
-# output = output.transpose(1, 2)
-# output = torch.relu(output)
-# output = self.cif_output(output)
-# alphas = torch.sigmoid(output)
-# alphas = torch.nn.functional.relu(alphas*self.smooth_factor - self.noise_threshold)
-# if mask is not None:
-# alphas = alphas * mask.transpose(-1, -2).float()
-# if mask_chunk_predictor is not None:
-# alphas = alphas * mask_chunk_predictor
-# alphas = alphas.squeeze(-1)
-# if target_label_length is not None:
-# target_length = target_label_length
-# elif target_label is not None:
-# target_length = (target_label != ignore_id).float().sum(-1)
-# # logging.info("target_length: {}".format(target_length))
-# else:
-# target_length = None
-# token_num = alphas.sum(-1)
-# if target_length is not None:
-# # length_noise = torch.rand(alphas.size(0), device=alphas.device) - 0.5
-# # target_length = length_noise + target_length
-# alphas *= ((target_length + 1e-4) / token_num)[:, None].repeat(1, alphas.size(1))
-# acoustic_embeds, cif_peak = self.cif(hidden, alphas, self.threshold, self.return_accum)
-# return acoustic_embeds, token_num, alphas, cif_peak
diff --git a/funasr/models/bat/conformer_chunk_encoder.py b/funasr/models/bat/conformer_chunk_encoder.py
deleted file mode 100644
index 7635c02..0000000
--- a/funasr/models/bat/conformer_chunk_encoder.py
+++ /dev/null
@@ -1,701 +0,0 @@
-
-"""Conformer encoder definition."""
-
-import logging
-from typing import Union, Dict, List, Tuple, Optional
-
-import torch
-from torch import nn
-
-
-from funasr.models.bat.attention import (
- RelPositionMultiHeadedAttentionChunk,
-)
-from funasr.models.transformer.embedding import (
- StreamingRelPositionalEncoding,
-)
-from funasr.models.transformer.layer_norm import LayerNorm
-from funasr.models.transformer.utils.nets_utils import get_activation
-from funasr.models.transformer.utils.nets_utils import (
- TooShortUttError,
- check_short_utt,
- make_chunk_mask,
- make_source_mask,
-)
-from funasr.models.transformer.positionwise_feed_forward import (
- PositionwiseFeedForward,
-)
-from funasr.models.transformer.utils.repeat import repeat, MultiBlocks
-from funasr.models.transformer.utils.subsampling import TooShortUttError
-from funasr.models.transformer.utils.subsampling import check_short_utt
-from funasr.models.transformer.utils.subsampling import StreamingConvInput
-from funasr.register import tables
-
-
-
-class ChunkEncoderLayer(nn.Module):
- """Chunk Conformer module definition.
- Args:
- block_size: Input/output size.
- self_att: Self-attention module instance.
- feed_forward: Feed-forward module instance.
- feed_forward_macaron: Feed-forward module instance for macaron network.
- conv_mod: Convolution module instance.
- norm_class: Normalization module class.
- norm_args: Normalization module arguments.
- dropout_rate: Dropout rate.
- """
-
- def __init__(
- self,
- block_size: int,
- self_att: torch.nn.Module,
- feed_forward: torch.nn.Module,
- feed_forward_macaron: torch.nn.Module,
- conv_mod: torch.nn.Module,
- norm_class: torch.nn.Module = LayerNorm,
- norm_args: Dict = {},
- dropout_rate: float = 0.0,
- ) -> None:
- """Construct a Conformer object."""
- super().__init__()
-
- self.self_att = self_att
-
- self.feed_forward = feed_forward
- self.feed_forward_macaron = feed_forward_macaron
- self.feed_forward_scale = 0.5
-
- self.conv_mod = conv_mod
-
- self.norm_feed_forward = norm_class(block_size, **norm_args)
- self.norm_self_att = norm_class(block_size, **norm_args)
-
- self.norm_macaron = norm_class(block_size, **norm_args)
- self.norm_conv = norm_class(block_size, **norm_args)
- self.norm_final = norm_class(block_size, **norm_args)
-
- self.dropout = torch.nn.Dropout(dropout_rate)
-
- self.block_size = block_size
- self.cache = None
-
- def reset_streaming_cache(self, left_context: int, device: torch.device) -> None:
- """Initialize/Reset self-attention and convolution modules cache for streaming.
- Args:
- left_context: Number of left frames during chunk-by-chunk inference.
- device: Device to use for cache tensor.
- """
- self.cache = [
- torch.zeros(
- (1, left_context, self.block_size),
- device=device,
- ),
- torch.zeros(
- (
- 1,
- self.block_size,
- self.conv_mod.kernel_size - 1,
- ),
- device=device,
- ),
- ]
-
- def forward(
- self,
- x: torch.Tensor,
- pos_enc: torch.Tensor,
- mask: torch.Tensor,
- chunk_mask: Optional[torch.Tensor] = None,
- ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
- """Encode input sequences.
- Args:
- x: Conformer input sequences. (B, T, D_block)
- pos_enc: Positional embedding sequences. (B, 2 * (T - 1), D_block)
- mask: Source mask. (B, T)
- chunk_mask: Chunk mask. (T_2, T_2)
- Returns:
- x: Conformer output sequences. (B, T, D_block)
- mask: Source mask. (B, T)
- pos_enc: Positional embedding sequences. (B, 2 * (T - 1), D_block)
- """
- residual = x
-
- x = self.norm_macaron(x)
- x = residual + self.feed_forward_scale * self.dropout(
- self.feed_forward_macaron(x)
- )
-
- residual = x
- x = self.norm_self_att(x)
- x_q = x
- x = residual + self.dropout(
- self.self_att(
- x_q,
- x,
- x,
- pos_enc,
- mask,
- chunk_mask=chunk_mask,
- )
- )
-
- residual = x
-
- x = self.norm_conv(x)
- x, _ = self.conv_mod(x)
- x = residual + self.dropout(x)
- residual = x
-
- x = self.norm_feed_forward(x)
- x = residual + self.feed_forward_scale * self.dropout(self.feed_forward(x))
-
- x = self.norm_final(x)
- return x, mask, pos_enc
-
- def chunk_forward(
- self,
- x: torch.Tensor,
- pos_enc: torch.Tensor,
- mask: torch.Tensor,
- chunk_size: int = 16,
- left_context: int = 0,
- right_context: int = 0,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """Encode chunk of input sequence.
- Args:
- x: Conformer input sequences. (B, T, D_block)
- pos_enc: Positional embedding sequences. (B, 2 * (T - 1), D_block)
- mask: Source mask. (B, T_2)
- left_context: Number of frames in left context.
- right_context: Number of frames in right context.
- Returns:
- x: Conformer output sequences. (B, T, D_block)
- pos_enc: Positional embedding sequences. (B, 2 * (T - 1), D_block)
- """
- residual = x
-
- x = self.norm_macaron(x)
- x = residual + self.feed_forward_scale * self.feed_forward_macaron(x)
-
- residual = x
- x = self.norm_self_att(x)
- if left_context > 0:
- key = torch.cat([self.cache[0], x], dim=1)
- else:
- key = x
- val = key
-
- if right_context > 0:
- att_cache = key[:, -(left_context + right_context) : -right_context, :]
- else:
- att_cache = key[:, -left_context:, :]
- x = residual + self.self_att(
- x,
- key,
- val,
- pos_enc,
- mask,
- left_context=left_context,
- )
-
- residual = x
- x = self.norm_conv(x)
- x, conv_cache = self.conv_mod(
- x, cache=self.cache[1], right_context=right_context
- )
- x = residual + x
- residual = x
-
- x = self.norm_feed_forward(x)
- x = residual + self.feed_forward_scale * self.feed_forward(x)
-
- x = self.norm_final(x)
- self.cache = [att_cache, conv_cache]
-
- return x, pos_enc
-
-
-
-class CausalConvolution(nn.Module):
- """ConformerConvolution module definition.
- Args:
- channels: The number of channels.
- kernel_size: Size of the convolving kernel.
- activation: Type of activation function.
- norm_args: Normalization module arguments.
- causal: Whether to use causal convolution (set to True if streaming).
- """
-
- def __init__(
- self,
- channels: int,
- kernel_size: int,
- activation: torch.nn.Module = torch.nn.ReLU(),
- norm_args: Dict = {},
- causal: bool = False,
- ) -> None:
- """Construct an ConformerConvolution object."""
- super().__init__()
-
- assert (kernel_size - 1) % 2 == 0
-
- self.kernel_size = kernel_size
-
- self.pointwise_conv1 = torch.nn.Conv1d(
- channels,
- 2 * channels,
- kernel_size=1,
- stride=1,
- padding=0,
- )
-
- if causal:
- self.lorder = kernel_size - 1
- padding = 0
- else:
- self.lorder = 0
- padding = (kernel_size - 1) // 2
-
- self.depthwise_conv = torch.nn.Conv1d(
- channels,
- channels,
- kernel_size,
- stride=1,
- padding=padding,
- groups=channels,
- )
- self.norm = torch.nn.BatchNorm1d(channels, **norm_args)
- self.pointwise_conv2 = torch.nn.Conv1d(
- channels,
- channels,
- kernel_size=1,
- stride=1,
- padding=0,
- )
-
- self.activation = activation
-
- def forward(
- self,
- x: torch.Tensor,
- cache: Optional[torch.Tensor] = None,
- right_context: int = 0,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """Compute convolution module.
- Args:
- x: ConformerConvolution input sequences. (B, T, D_hidden)
- cache: ConformerConvolution input cache. (1, conv_kernel, D_hidden)
- right_context: Number of frames in right context.
- Returns:
- x: ConformerConvolution output sequences. (B, T, D_hidden)
- cache: ConformerConvolution output cache. (1, conv_kernel, D_hidden)
- """
- x = self.pointwise_conv1(x.transpose(1, 2))
- x = torch.nn.functional.glu(x, dim=1)
-
- if self.lorder > 0:
- if cache is None:
- x = torch.nn.functional.pad(x, (self.lorder, 0), "constant", 0.0)
- else:
- x = torch.cat([cache, x], dim=2)
-
- if right_context > 0:
- cache = x[:, :, -(self.lorder + right_context) : -right_context]
- else:
- cache = x[:, :, -self.lorder :]
-
- x = self.depthwise_conv(x)
- x = self.activation(self.norm(x))
-
- x = self.pointwise_conv2(x).transpose(1, 2)
-
- return x, cache
-
-@tables.register("encoder_classes", "ConformerChunkEncoder")
-class ConformerChunkEncoder(nn.Module):
- """Encoder module definition.
- Args:
- input_size: Input size.
- body_conf: Encoder body configuration.
- input_conf: Encoder input configuration.
- main_conf: Encoder main configuration.
- """
-
- def __init__(
- self,
- input_size: int,
- output_size: int = 256,
- attention_heads: int = 4,
- linear_units: int = 2048,
- num_blocks: int = 6,
- dropout_rate: float = 0.1,
- positional_dropout_rate: float = 0.1,
- attention_dropout_rate: float = 0.0,
- embed_vgg_like: bool = False,
- normalize_before: bool = True,
- concat_after: bool = False,
- positionwise_layer_type: str = "linear",
- positionwise_conv_kernel_size: int = 3,
- macaron_style: bool = False,
- rel_pos_type: str = "legacy",
- pos_enc_layer_type: str = "rel_pos",
- selfattention_layer_type: str = "rel_selfattn",
- activation_type: str = "swish",
- use_cnn_module: bool = True,
- zero_triu: bool = False,
- norm_type: str = "layer_norm",
- cnn_module_kernel: int = 31,
- conv_mod_norm_eps: float = 0.00001,
- conv_mod_norm_momentum: float = 0.1,
- simplified_att_score: bool = False,
- dynamic_chunk_training: bool = False,
- short_chunk_threshold: float = 0.75,
- short_chunk_size: int = 25,
- left_chunk_size: int = 0,
- time_reduction_factor: int = 1,
- unified_model_training: bool = False,
- default_chunk_size: int = 16,
- jitter_range: int = 4,
- subsampling_factor: int = 1,
- ) -> None:
- """Construct an Encoder object."""
- super().__init__()
-
-
- self.embed = StreamingConvInput(
- input_size,
- output_size,
- subsampling_factor,
- vgg_like=embed_vgg_like,
- output_size=output_size,
- )
-
- self.pos_enc = StreamingRelPositionalEncoding(
- output_size,
- positional_dropout_rate,
- )
-
- activation = get_activation(
- activation_type
- )
-
- pos_wise_args = (
- output_size,
- linear_units,
- positional_dropout_rate,
- activation,
- )
-
- conv_mod_norm_args = {
- "eps": conv_mod_norm_eps,
- "momentum": conv_mod_norm_momentum,
- }
-
- conv_mod_args = (
- output_size,
- cnn_module_kernel,
- activation,
- conv_mod_norm_args,
- dynamic_chunk_training or unified_model_training,
- )
-
- mult_att_args = (
- attention_heads,
- output_size,
- attention_dropout_rate,
- simplified_att_score,
- )
-
-
- fn_modules = []
- for _ in range(num_blocks):
- module = lambda: ChunkEncoderLayer(
- output_size,
- RelPositionMultiHeadedAttentionChunk(*mult_att_args),
- PositionwiseFeedForward(*pos_wise_args),
- PositionwiseFeedForward(*pos_wise_args),
- CausalConvolution(*conv_mod_args),
- dropout_rate=dropout_rate,
- )
- fn_modules.append(module)
-
- self.encoders = MultiBlocks(
- [fn() for fn in fn_modules],
- output_size,
- )
-
- self._output_size = output_size
-
- self.dynamic_chunk_training = dynamic_chunk_training
- self.short_chunk_threshold = short_chunk_threshold
- self.short_chunk_size = short_chunk_size
- self.left_chunk_size = left_chunk_size
-
- self.unified_model_training = unified_model_training
- self.default_chunk_size = default_chunk_size
- self.jitter_range = jitter_range
-
- self.time_reduction_factor = time_reduction_factor
-
- def output_size(self) -> int:
- return self._output_size
-
- def get_encoder_input_raw_size(self, size: int, hop_length: int) -> int:
- """Return the corresponding number of sample for a given chunk size, in frames.
- Where size is the number of features frames after applying subsampling.
- Args:
- size: Number of frames after subsampling.
- hop_length: Frontend's hop length
- Returns:
- : Number of raw samples
- """
- return self.embed.get_size_before_subsampling(size) * hop_length
-
- def get_encoder_input_size(self, size: int) -> int:
- """Return the corresponding number of sample for a given chunk size, in frames.
- Where size is the number of features frames after applying subsampling.
- Args:
- size: Number of frames after subsampling.
- Returns:
- : Number of raw samples
- """
- return self.embed.get_size_before_subsampling(size)
-
-
- def reset_streaming_cache(self, left_context: int, device: torch.device) -> None:
- """Initialize/Reset encoder streaming cache.
- Args:
- left_context: Number of frames in left context.
- device: Device ID.
- """
- return self.encoders.reset_streaming_cache(left_context, device)
-
- def forward(
- self,
- x: torch.Tensor,
- x_len: torch.Tensor,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """Encode input sequences.
- Args:
- x: Encoder input features. (B, T_in, F)
- x_len: Encoder input features lengths. (B,)
- Returns:
- x: Encoder outputs. (B, T_out, D_enc)
- x_len: Encoder outputs lenghts. (B,)
- """
- short_status, limit_size = check_short_utt(
- self.embed.subsampling_factor, x.size(1)
- )
-
- if short_status:
- raise TooShortUttError(
- f"has {x.size(1)} frames and is too short for subsampling "
- + f"(it needs more than {limit_size} frames), return empty results",
- x.size(1),
- limit_size,
- )
-
- mask = make_source_mask(x_len).to(x.device)
-
- if self.unified_model_training:
- if self.training:
- chunk_size = self.default_chunk_size + torch.randint(-self.jitter_range, self.jitter_range+1, (1,)).item()
- else:
- chunk_size = self.default_chunk_size
- x, mask = self.embed(x, mask, chunk_size)
- pos_enc = self.pos_enc(x)
- chunk_mask = make_chunk_mask(
- x.size(1),
- chunk_size,
- left_chunk_size=self.left_chunk_size,
- device=x.device,
- )
- x_utt = self.encoders(
- x,
- pos_enc,
- mask,
- chunk_mask=None,
- )
- x_chunk = self.encoders(
- x,
- pos_enc,
- mask,
- chunk_mask=chunk_mask,
- )
-
- olens = mask.eq(0).sum(1)
- if self.time_reduction_factor > 1:
- x_utt = x_utt[:,::self.time_reduction_factor,:]
- x_chunk = x_chunk[:,::self.time_reduction_factor,:]
- olens = torch.floor_divide(olens-1, self.time_reduction_factor) + 1
-
- return x_utt, x_chunk, olens
-
- elif self.dynamic_chunk_training:
- max_len = x.size(1)
- if self.training:
- chunk_size = torch.randint(1, max_len, (1,)).item()
-
- if chunk_size > (max_len * self.short_chunk_threshold):
- chunk_size = max_len
- else:
- chunk_size = (chunk_size % self.short_chunk_size) + 1
- else:
- chunk_size = self.default_chunk_size
-
- x, mask = self.embed(x, mask, chunk_size)
- pos_enc = self.pos_enc(x)
-
- chunk_mask = make_chunk_mask(
- x.size(1),
- chunk_size,
- left_chunk_size=self.left_chunk_size,
- device=x.device,
- )
- else:
- x, mask = self.embed(x, mask, None)
- pos_enc = self.pos_enc(x)
- chunk_mask = None
- x = self.encoders(
- x,
- pos_enc,
- mask,
- chunk_mask=chunk_mask,
- )
-
- olens = mask.eq(0).sum(1)
- if self.time_reduction_factor > 1:
- x = x[:,::self.time_reduction_factor,:]
- olens = torch.floor_divide(olens-1, self.time_reduction_factor) + 1
-
- return x, olens, None
-
- def full_utt_forward(
- self,
- x: torch.Tensor,
- x_len: torch.Tensor,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """Encode input sequences.
- Args:
- x: Encoder input features. (B, T_in, F)
- x_len: Encoder input features lengths. (B,)
- Returns:
- x: Encoder outputs. (B, T_out, D_enc)
- x_len: Encoder outputs lenghts. (B,)
- """
- short_status, limit_size = check_short_utt(
- self.embed.subsampling_factor, x.size(1)
- )
-
- if short_status:
- raise TooShortUttError(
- f"has {x.size(1)} frames and is too short for subsampling "
- + f"(it needs more than {limit_size} frames), return empty results",
- x.size(1),
- limit_size,
- )
-
- mask = make_source_mask(x_len).to(x.device)
- x, mask = self.embed(x, mask, None)
- pos_enc = self.pos_enc(x)
- x_utt = self.encoders(
- x,
- pos_enc,
- mask,
- chunk_mask=None,
- )
-
- if self.time_reduction_factor > 1:
- x_utt = x_utt[:,::self.time_reduction_factor,:]
- return x_utt
-
- def simu_chunk_forward(
- self,
- x: torch.Tensor,
- x_len: torch.Tensor,
- chunk_size: int = 16,
- left_context: int = 32,
- right_context: int = 0,
- ) -> torch.Tensor:
- short_status, limit_size = check_short_utt(
- self.embed.subsampling_factor, x.size(1)
- )
-
- if short_status:
- raise TooShortUttError(
- f"has {x.size(1)} frames and is too short for subsampling "
- + f"(it needs more than {limit_size} frames), return empty results",
- x.size(1),
- limit_size,
- )
-
- mask = make_source_mask(x_len)
-
- x, mask = self.embed(x, mask, chunk_size)
- pos_enc = self.pos_enc(x)
- chunk_mask = make_chunk_mask(
- x.size(1),
- chunk_size,
- left_chunk_size=self.left_chunk_size,
- device=x.device,
- )
-
- x = self.encoders(
- x,
- pos_enc,
- mask,
- chunk_mask=chunk_mask,
- )
- olens = mask.eq(0).sum(1)
- if self.time_reduction_factor > 1:
- x = x[:,::self.time_reduction_factor,:]
-
- return x
-
- def chunk_forward(
- self,
- x: torch.Tensor,
- x_len: torch.Tensor,
- processed_frames: torch.tensor,
- chunk_size: int = 16,
- left_context: int = 32,
- right_context: int = 0,
- ) -> torch.Tensor:
- """Encode input sequences as chunks.
- Args:
- x: Encoder input features. (1, T_in, F)
- x_len: Encoder input features lengths. (1,)
- processed_frames: Number of frames already seen.
- left_context: Number of frames in left context.
- right_context: Number of frames in right context.
- Returns:
- x: Encoder outputs. (B, T_out, D_enc)
- """
- mask = make_source_mask(x_len)
- x, mask = self.embed(x, mask, None)
-
- if left_context > 0:
- processed_mask = (
- torch.arange(left_context, device=x.device)
- .view(1, left_context)
- .flip(1)
- )
- processed_mask = processed_mask >= processed_frames
- mask = torch.cat([processed_mask, mask], dim=1)
- pos_enc = self.pos_enc(x, left_context=left_context)
- x = self.encoders.chunk_forward(
- x,
- pos_enc,
- mask,
- chunk_size=chunk_size,
- left_context=left_context,
- right_context=right_context,
- )
-
- if right_context > 0:
- x = x[:, 0:-right_context, :]
-
- if self.time_reduction_factor > 1:
- x = x[:,::self.time_reduction_factor,:]
- return x
diff --git a/funasr/models/bat/model.py b/funasr/models/bat/model.py
index 3fed9aa..8e76b45 100644
--- a/funasr/models/bat/model.py
+++ b/funasr/models/bat/model.py
@@ -3,137 +3,145 @@
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
# MIT License (https://opensource.org/licenses/MIT)
-
+import time
import torch
import logging
-import torch.nn as nn
+from contextlib import contextmanager
+from typing import Dict, Optional, Tuple
+from distutils.version import LooseVersion
-from typing import Dict, List, Optional, Tuple, Union
-
-
-from torch.cuda.amp import autocast
-from funasr.losses.label_smoothing_loss import (
- LabelSmoothingLoss, # noqa: H301
-)
-
-from funasr.models.transformer.utils.nets_utils import get_transducer_task_io
-from funasr.models.transformer.utils.nets_utils import make_pad_mask
-from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
+from funasr.register import tables
+from funasr.utils import postprocess_utils
+from funasr.utils.datadir_writer import DatadirWriter
from funasr.train_utils.device_funcs import force_gatherable
+from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
+from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
+from funasr.models.transformer.scorers.length_bonus import LengthBonus
+from funasr.models.transformer.utils.nets_utils import get_transducer_task_io
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+from funasr.models.transducer.beam_search_transducer import BeamSearchTransducer
+if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
+ from torch.cuda.amp import autocast
+else:
+ # Nothing to do if torch<1.6.0
+ @contextmanager
+ def autocast(enabled=True):
+ yield
-
-class BATModel(nn.Module):
- """BATModel module definition.
-
- Args:
- vocab_size: Size of complete vocabulary (w/ EOS and blank included).
- token_list: List of token
- frontend: Frontend module.
- specaug: SpecAugment module.
- normalize: Normalization module.
- encoder: Encoder module.
- decoder: Decoder module.
- joint_network: Joint Network module.
- transducer_weight: Weight of the Transducer loss.
- fastemit_lambda: FastEmit lambda value.
- auxiliary_ctc_weight: Weight of auxiliary CTC loss.
- auxiliary_ctc_dropout_rate: Dropout rate for auxiliary CTC loss inputs.
- auxiliary_lm_loss_weight: Weight of auxiliary LM loss.
- auxiliary_lm_loss_smoothing: Smoothing rate for LM loss' label smoothing.
- ignore_id: Initial padding ID.
- sym_space: Space symbol.
- sym_blank: Blank Symbol
- report_cer: Whether to report Character Error Rate during validation.
- report_wer: Whether to report Word Error Rate during validation.
- extract_feats_in_collect_stats: Whether to use extract_feats stats collection.
-
- """
-
+@tables.register("model_classes", "BAT") # TODO: BAT training
+class BAT(torch.nn.Module):
def __init__(
self,
-
- cif_weight: float = 1.0,
+ frontend: Optional[str] = None,
+ frontend_conf: Optional[Dict] = None,
+ specaug: Optional[str] = None,
+ specaug_conf: Optional[Dict] = None,
+ normalize: str = None,
+ normalize_conf: Optional[Dict] = None,
+ encoder: str = None,
+ encoder_conf: Optional[Dict] = None,
+ decoder: str = None,
+ decoder_conf: Optional[Dict] = None,
+ joint_network: str = None,
+ joint_network_conf: Optional[Dict] = None,
+ transducer_weight: float = 1.0,
fastemit_lambda: float = 0.0,
auxiliary_ctc_weight: float = 0.0,
auxiliary_ctc_dropout_rate: float = 0.0,
auxiliary_lm_loss_weight: float = 0.0,
auxiliary_lm_loss_smoothing: float = 0.0,
+ input_size: int = 80,
+ vocab_size: int = -1,
ignore_id: int = -1,
- sym_space: str = "<space>",
- sym_blank: str = "<blank>",
- report_cer: bool = True,
- report_wer: bool = True,
- extract_feats_in_collect_stats: bool = True,
+ blank_id: int = 0,
+ sos: int = 1,
+ eos: int = 2,
lsm_weight: float = 0.0,
length_normalized_loss: bool = False,
- r_d: int = 5,
- r_u: int = 5,
+ # report_cer: bool = True,
+ # report_wer: bool = True,
+ # sym_space: str = "<space>",
+ # sym_blank: str = "<blank>",
+ # extract_feats_in_collect_stats: bool = True,
+ share_embedding: bool = False,
+ # preencoder: Optional[AbsPreEncoder] = None,
+ # postencoder: Optional[AbsPostEncoder] = None,
**kwargs,
- ) -> None:
- """Construct an BATModel object."""
+ ):
+
super().__init__()
- # The following labels ID are reserved: 0 (blank) and vocab_size - 1 (sos/eos)
- self.blank_id = 0
+ if specaug is not None:
+ specaug_class = tables.specaug_classes.get(specaug)
+ specaug = specaug_class(**specaug_conf)
+ if normalize is not None:
+ normalize_class = tables.normalize_classes.get(normalize)
+ normalize = normalize_class(**normalize_conf)
+ encoder_class = tables.encoder_classes.get(encoder)
+ encoder = encoder_class(input_size=input_size, **encoder_conf)
+ encoder_output_size = encoder.output_size()
+
+ decoder_class = tables.decoder_classes.get(decoder)
+ decoder = decoder_class(
+ vocab_size=vocab_size,
+ **decoder_conf,
+ )
+ decoder_output_size = decoder.output_size
+
+ joint_network_class = tables.joint_network_classes.get(joint_network)
+ joint_network = joint_network_class(
+ vocab_size,
+ encoder_output_size,
+ decoder_output_size,
+ **joint_network_conf,
+ )
+
+ self.criterion_transducer = None
+ self.error_calculator = None
+
+ self.use_auxiliary_ctc = auxiliary_ctc_weight > 0
+ self.use_auxiliary_lm_loss = auxiliary_lm_loss_weight > 0
+
+ if self.use_auxiliary_ctc:
+ self.ctc_lin = torch.nn.Linear(encoder.output_size(), vocab_size)
+ self.ctc_dropout_rate = auxiliary_ctc_dropout_rate
+
+ if self.use_auxiliary_lm_loss:
+ self.lm_lin = torch.nn.Linear(decoder.output_size, vocab_size)
+ self.lm_loss_smoothing = auxiliary_lm_loss_smoothing
+
+ self.transducer_weight = transducer_weight
+ self.fastemit_lambda = fastemit_lambda
+
+ self.auxiliary_ctc_weight = auxiliary_ctc_weight
+ self.auxiliary_lm_loss_weight = auxiliary_lm_loss_weight
+ self.blank_id = blank_id
+ self.sos = sos if sos is not None else vocab_size - 1
+ self.eos = eos if eos is not None else vocab_size - 1
self.vocab_size = vocab_size
self.ignore_id = ignore_id
- self.token_list = token_list.copy()
-
- self.sym_space = sym_space
- self.sym_blank = sym_blank
-
self.frontend = frontend
self.specaug = specaug
self.normalize = normalize
-
self.encoder = encoder
self.decoder = decoder
self.joint_network = joint_network
- self.criterion_transducer = None
- self.error_calculator = None
+ self.criterion_att = LabelSmoothingLoss(
+ size=vocab_size,
+ padding_idx=ignore_id,
+ smoothing=lsm_weight,
+ normalize_length=length_normalized_loss,
+ )
- self.use_auxiliary_ctc = auxiliary_ctc_weight > 0
- self.use_auxiliary_lm_loss = auxiliary_lm_loss_weight > 0
-
- if self.use_auxiliary_ctc:
- self.ctc_lin = torch.nn.Linear(encoder.output_size(), vocab_size)
- self.ctc_dropout_rate = auxiliary_ctc_dropout_rate
-
- if self.use_auxiliary_lm_loss:
- self.lm_lin = torch.nn.Linear(decoder.output_size, vocab_size)
- self.lm_loss_smoothing = auxiliary_lm_loss_smoothing
-
- self.transducer_weight = transducer_weight
- self.fastemit_lambda = fastemit_lambda
-
- self.auxiliary_ctc_weight = auxiliary_ctc_weight
- self.auxiliary_lm_loss_weight = auxiliary_lm_loss_weight
-
- self.report_cer = report_cer
- self.report_wer = report_wer
-
- self.extract_feats_in_collect_stats = extract_feats_in_collect_stats
-
- self.criterion_pre = torch.nn.L1Loss()
- self.predictor_weight = predictor_weight
- self.predictor = predictor
-
- self.cif_weight = cif_weight
- if self.cif_weight > 0:
- self.cif_output_layer = torch.nn.Linear(encoder.output_size(), vocab_size)
- self.criterion_cif = LabelSmoothingLoss(
- size=vocab_size,
- padding_idx=ignore_id,
- smoothing=lsm_weight,
- normalize_length=length_normalized_loss,
- )
- self.r_d = r_d
- self.r_u = r_u
-
+ self.length_normalized_loss = length_normalized_loss
+ self.beam_search = None
+ self.ctc = None
+ self.ctc_weight = 0.0
+
def forward(
self,
speech: torch.Tensor,
@@ -142,111 +150,167 @@
text_lengths: torch.Tensor,
**kwargs,
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
- """Forward architecture and compute loss(es).
-
+ """Encoder + Decoder + Calc loss
Args:
- speech: Speech sequences. (B, S)
- speech_lengths: Speech sequences lengths. (B,)
- text: Label ID sequences. (B, L)
- text_lengths: Label ID sequences lengths. (B,)
- kwargs: Contains "utts_id".
-
- Return:
- loss: Main loss value.
- stats: Task statistics.
- weight: Task weights.
-
+ speech: (Batch, Length, ...)
+ speech_lengths: (Batch, )
+ text: (Batch, Length)
+ text_lengths: (Batch,)
"""
- assert text_lengths.dim() == 1, text_lengths.shape
- assert (
- speech.shape[0]
- == speech_lengths.shape[0]
- == text.shape[0]
- == text_lengths.shape[0]
- ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
-
+ if len(text_lengths.size()) > 1:
+ text_lengths = text_lengths[:, 0]
+ if len(speech_lengths.size()) > 1:
+ speech_lengths = speech_lengths[:, 0]
+
batch_size = speech.shape[0]
- text = text[:, : text_lengths.max()]
-
# 1. Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
if hasattr(self.encoder, 'overlap_chunk_cls') and self.encoder.overlap_chunk_cls is not None:
encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
chunk_outs=None)
-
- encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(encoder_out.device)
# 2. Transducer-related I/O preparation
decoder_in, target, t_len, u_len = get_transducer_task_io(
text,
encoder_out_lens,
ignore_id=self.ignore_id,
)
-
+
# 3. Decoder
self.decoder.set_device(encoder_out.device)
decoder_out = self.decoder(decoder_in, u_len)
-
- pre_acoustic_embeds, pre_token_length, _, pre_peak_index = self.predictor(encoder_out, text, encoder_out_mask, ignore_id=self.ignore_id)
- loss_pre = self.criterion_pre(text_lengths.type_as(pre_token_length), pre_token_length)
-
- if self.cif_weight > 0.0:
- cif_predict = self.cif_output_layer(pre_acoustic_embeds)
- loss_cif = self.criterion_cif(cif_predict, text)
- else:
- loss_cif = 0.0
-
- # 5. Losses
- boundary = torch.zeros((encoder_out.size(0), 4), dtype=torch.int64, device=encoder_out.device)
- boundary[:, 2] = u_len.long().detach()
- boundary[:, 3] = t_len.long().detach()
-
- pre_peak_index = torch.floor(pre_peak_index).long()
- s_begin = pre_peak_index - self.r_d
-
- T = encoder_out.size(1)
- B = encoder_out.size(0)
- U = decoder_out.size(1)
-
- mask = torch.arange(0, T, device=encoder_out.device).reshape(1, T).expand(B, T)
- mask = mask <= boundary[:, 3].reshape(B, 1) - 1
-
- s_begin_padding = boundary[:, 2].reshape(B, 1) - (self.r_u+self.r_d) + 1
- # handle the cases where `len(symbols) < s_range`
- s_begin_padding = torch.clamp(s_begin_padding, min=0)
-
- s_begin = torch.where(mask, s_begin, s_begin_padding)
- mask2 = s_begin < boundary[:, 2].reshape(B, 1) - (self.r_u+self.r_d) + 1
-
- s_begin = torch.where(mask2, s_begin, boundary[:, 2].reshape(B, 1) - (self.r_u+self.r_d) + 1)
-
- s_begin = torch.clamp(s_begin, min=0)
-
- ranges = s_begin.reshape((B, T, 1)).expand((B, T, min(self.r_u+self.r_d, min(u_len)))) + torch.arange(min(self.r_d+self.r_u, min(u_len)), device=encoder_out.device)
-
- import fast_rnnt
- am_pruned, lm_pruned = fast_rnnt.do_rnnt_pruning(
- am=self.joint_network.lin_enc(encoder_out),
- lm=self.joint_network.lin_dec(decoder_out),
- ranges=ranges,
+ # 4. Joint Network
+ joint_out = self.joint_network(
+ encoder_out.unsqueeze(2), decoder_out.unsqueeze(1)
)
-
- logits = self.joint_network(am_pruned, lm_pruned, project_input=False)
-
- with torch.cuda.amp.autocast(enabled=False):
- loss_trans = fast_rnnt.rnnt_loss_pruned(
- logits=logits.float(),
- symbols=target.long(),
- ranges=ranges,
- termination_symbol=self.blank_id,
- boundary=boundary,
- reduction="sum",
+
+ # 5. Losses
+ loss_trans, cer_trans, wer_trans = self._calc_transducer_loss(
+ encoder_out,
+ joint_out,
+ target,
+ t_len,
+ u_len,
+ )
+
+ loss_ctc, loss_lm = 0.0, 0.0
+
+ if self.use_auxiliary_ctc:
+ loss_ctc = self._calc_ctc_loss(
+ encoder_out,
+ target,
+ t_len,
+ u_len,
)
+
+ if self.use_auxiliary_lm_loss:
+ loss_lm = self._calc_lm_loss(decoder_out, target)
+
+ loss = (
+ self.transducer_weight * loss_trans
+ + self.auxiliary_ctc_weight * loss_ctc
+ + self.auxiliary_lm_loss_weight * loss_lm
+ )
+
+ stats = dict(
+ loss=loss.detach(),
+ loss_transducer=loss_trans.detach(),
+ aux_ctc_loss=loss_ctc.detach() if loss_ctc > 0.0 else None,
+ aux_lm_loss=loss_lm.detach() if loss_lm > 0.0 else None,
+ cer_transducer=cer_trans,
+ wer_transducer=wer_trans,
+ )
+
+ # force_gatherable: to-device and to-tensor if scalar for DataParallel
+ loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
+
+ return loss, stats, weight
- cer_trans, wer_trans = None, None
+ def encode(
+ self, speech: torch.Tensor, speech_lengths: torch.Tensor, **kwargs,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Frontend + Encoder. Note that this method is used by asr_inference.py
+ Args:
+ speech: (Batch, Length, ...)
+ speech_lengths: (Batch, )
+ ind: int
+ """
+ with autocast(False):
+
+ # Data augmentation
+ if self.specaug is not None and self.training:
+ speech, speech_lengths = self.specaug(speech, speech_lengths)
+
+ # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
+ if self.normalize is not None:
+ speech, speech_lengths = self.normalize(speech, speech_lengths)
+
+ # Forward encoder
+ # feats: (Batch, Length, Dim)
+ # -> encoder_out: (Batch, Length2, Dim2)
+ encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
+ intermediate_outs = None
+ if isinstance(encoder_out, tuple):
+ intermediate_outs = encoder_out[1]
+ encoder_out = encoder_out[0]
+
+ if intermediate_outs is not None:
+ return (encoder_out, intermediate_outs), encoder_out_lens
+
+ return encoder_out, encoder_out_lens
+
+ def _calc_transducer_loss(
+ self,
+ encoder_out: torch.Tensor,
+ joint_out: torch.Tensor,
+ target: torch.Tensor,
+ t_len: torch.Tensor,
+ u_len: torch.Tensor,
+ ) -> Tuple[torch.Tensor, Optional[float], Optional[float]]:
+ """Compute Transducer loss.
+
+ Args:
+ encoder_out: Encoder output sequences. (B, T, D_enc)
+ joint_out: Joint Network output sequences (B, T, U, D_joint)
+ target: Target label ID sequences. (B, L)
+ t_len: Encoder output sequences lengths. (B,)
+ u_len: Target label ID sequences lengths. (B,)
+
+ Return:
+ loss_transducer: Transducer loss value.
+ cer_transducer: Character error rate for Transducer.
+ wer_transducer: Word Error Rate for Transducer.
+
+ """
+ if self.criterion_transducer is None:
+ try:
+ from warp_rnnt import rnnt_loss as RNNTLoss
+ self.criterion_transducer = RNNTLoss
+
+ except ImportError:
+ logging.error(
+ "warp-rnnt was not installed."
+ "Please consult the installation documentation."
+ )
+ exit(1)
+
+ log_probs = torch.log_softmax(joint_out, dim=-1)
+
+ loss_transducer = self.criterion_transducer(
+ log_probs,
+ target,
+ t_len,
+ u_len,
+ reduction="mean",
+ blank=self.blank_id,
+ fastemit_lambda=self.fastemit_lambda,
+ gather=True,
+ )
+
if not self.training and (self.report_cer or self.report_wer):
if self.error_calculator is None:
from funasr.metrics import ErrorCalculatorTransducer as ErrorCalculator
+
self.error_calculator = ErrorCalculator(
self.decoder,
self.joint_network,
@@ -256,149 +320,13 @@
report_cer=self.report_cer,
report_wer=self.report_wer,
)
- cer_trans, wer_trans = self.error_calculator(encoder_out, target, t_len)
-
- loss_ctc, loss_lm = 0.0, 0.0
-
- if self.use_auxiliary_ctc:
- loss_ctc = self._calc_ctc_loss(
- encoder_out,
- target,
- t_len,
- u_len,
- )
-
- if self.use_auxiliary_lm_loss:
- loss_lm = self._calc_lm_loss(decoder_out, target)
-
- loss = (
- self.transducer_weight * loss_trans
- + self.auxiliary_ctc_weight * loss_ctc
- + self.auxiliary_lm_loss_weight * loss_lm
- + self.predictor_weight * loss_pre
- + self.cif_weight * loss_cif
- )
-
- stats = dict(
- loss=loss.detach(),
- loss_transducer=loss_trans.detach(),
- loss_pre=loss_pre.detach(),
- loss_cif=loss_cif.detach() if loss_cif > 0.0 else None,
- aux_ctc_loss=loss_ctc.detach() if loss_ctc > 0.0 else None,
- aux_lm_loss=loss_lm.detach() if loss_lm > 0.0 else None,
- cer_transducer=cer_trans,
- wer_transducer=wer_trans,
- )
-
- # force_gatherable: to-device and to-tensor if scalar for DataParallel
- loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
-
- return loss, stats, weight
-
- def collect_feats(
- self,
- speech: torch.Tensor,
- speech_lengths: torch.Tensor,
- text: torch.Tensor,
- text_lengths: torch.Tensor,
- **kwargs,
- ) -> Dict[str, torch.Tensor]:
- """Collect features sequences and features lengths sequences.
-
- Args:
- speech: Speech sequences. (B, S)
- speech_lengths: Speech sequences lengths. (B,)
- text: Label ID sequences. (B, L)
- text_lengths: Label ID sequences lengths. (B,)
- kwargs: Contains "utts_id".
-
- Return:
- {}: "feats": Features sequences. (B, T, D_feats),
- "feats_lengths": Features sequences lengths. (B,)
-
- """
- if self.extract_feats_in_collect_stats:
- feats, feats_lengths = self._extract_feats(speech, speech_lengths)
- else:
- # Generate dummy stats if extract_feats_in_collect_stats is False
- logging.warning(
- "Generating dummy stats for feats and feats_lengths, "
- "because encoder_conf.extract_feats_in_collect_stats is "
- f"{self.extract_feats_in_collect_stats}"
- )
-
- feats, feats_lengths = speech, speech_lengths
-
- return {"feats": feats, "feats_lengths": feats_lengths}
-
- def encode(
- self,
- speech: torch.Tensor,
- speech_lengths: torch.Tensor,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """Encoder speech sequences.
-
- Args:
- speech: Speech sequences. (B, S)
- speech_lengths: Speech sequences lengths. (B,)
-
- Return:
- encoder_out: Encoder outputs. (B, T, D_enc)
- encoder_out_lens: Encoder outputs lengths. (B,)
-
- """
- with autocast(False):
- # 1. Extract feats
- feats, feats_lengths = self._extract_feats(speech, speech_lengths)
-
- # 2. Data augmentation
- if self.specaug is not None and self.training:
- feats, feats_lengths = self.specaug(feats, feats_lengths)
-
- # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
- if self.normalize is not None:
- feats, feats_lengths = self.normalize(feats, feats_lengths)
-
- # 4. Forward encoder
- encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
-
- assert encoder_out.size(0) == speech.size(0), (
- encoder_out.size(),
- speech.size(0),
- )
- assert encoder_out.size(1) <= encoder_out_lens.max(), (
- encoder_out.size(),
- encoder_out_lens.max(),
- )
-
- return encoder_out, encoder_out_lens
-
- def _extract_feats(
- self, speech: torch.Tensor, speech_lengths: torch.Tensor
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """Extract features sequences and features sequences lengths.
-
- Args:
- speech: Speech sequences. (B, S)
- speech_lengths: Speech sequences lengths. (B,)
-
- Return:
- feats: Features sequences. (B, T, D_feats)
- feats_lengths: Features sequences lengths. (B,)
-
- """
- assert speech_lengths.dim() == 1, speech_lengths.shape
-
- # for data-parallel
- speech = speech[:, : speech_lengths.max()]
-
- if self.frontend is not None:
- feats, feats_lengths = self.frontend(speech, speech_lengths)
- else:
- feats, feats_lengths = speech, speech_lengths
-
- return feats, feats_lengths
-
+
+ cer_transducer, wer_transducer = self.error_calculator(encoder_out, target, t_len)
+
+ return loss_transducer, cer_transducer, wer_transducer
+
+ return loss_transducer, None, None
+
def _calc_ctc_loss(
self,
encoder_out: torch.Tensor,
@@ -422,10 +350,10 @@
torch.nn.functional.dropout(encoder_out, p=self.ctc_dropout_rate)
)
ctc_in = torch.log_softmax(ctc_in.transpose(0, 1), dim=-1)
-
+
target_mask = target != 0
ctc_target = target[target_mask].cpu()
-
+
with torch.backends.cudnn.flags(deterministic=True):
loss_ctc = torch.nn.functional.ctc_loss(
ctc_in,
@@ -436,9 +364,9 @@
reduction="sum",
)
loss_ctc /= target.size(0)
-
+
return loss_ctc
-
+
def _calc_lm_loss(
self,
decoder_out: torch.Tensor,
@@ -456,17 +384,17 @@
"""
lm_loss_in = self.lm_lin(decoder_out[:, :-1, :]).view(-1, self.vocab_size)
lm_target = target.view(-1).type(torch.int64)
-
+
with torch.no_grad():
true_dist = lm_loss_in.clone()
true_dist.fill_(self.lm_loss_smoothing / (self.vocab_size - 1))
-
+
# Ignore blank ID (0)
ignore = lm_target == 0
lm_target = lm_target.masked_fill(ignore, 0)
-
+
true_dist.scatter_(1, lm_target.unsqueeze(1), (1 - self.lm_loss_smoothing))
-
+
loss_lm = torch.nn.functional.kl_div(
torch.log_softmax(lm_loss_in, dim=1),
true_dist,
@@ -475,5 +403,117 @@
loss_lm = loss_lm.masked_fill(ignore.unsqueeze(1), 0).sum() / decoder_out.size(
0
)
-
+
return loss_lm
+
+ def init_beam_search(self,
+ **kwargs,
+ ):
+
+ # 1. Build ASR model
+ scorers = {}
+
+ if self.ctc != None:
+ ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos)
+ scorers.update(
+ ctc=ctc
+ )
+ token_list = kwargs.get("token_list")
+ scorers.update(
+ length_bonus=LengthBonus(len(token_list)),
+ )
+
+ # 3. Build ngram model
+ # ngram is not supported now
+ ngram = None
+ scorers["ngram"] = ngram
+
+ beam_search = BeamSearchTransducer(
+ self.decoder,
+ self.joint_network,
+ kwargs.get("beam_size", 2),
+ nbest=1,
+ )
+ # beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
+ # for scorer in scorers.values():
+ # if isinstance(scorer, torch.nn.Module):
+ # scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
+ self.beam_search = beam_search
+
+ def inference(self,
+ data_in: list,
+ data_lengths: list=None,
+ key: list=None,
+ tokenizer=None,
+ **kwargs,
+ ):
+
+ if kwargs.get("batch_size", 1) > 1:
+ raise NotImplementedError("batch decoding is not implemented")
+
+ # 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_text_image_video(data_in, fs=self.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=self.frontend)
+ time3 = time.perf_counter()
+ meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+ meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
+
+ speech = speech.to(device=kwargs["device"])
+ speech_lengths = speech_lengths.to(device=kwargs["device"])
+
+ # Encoder
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+ if isinstance(encoder_out, tuple):
+ encoder_out = encoder_out[0]
+
+ # c. Passed the encoder result and the beam search
+ nbest_hyps = self.beam_search(encoder_out[0], is_final=True)
+ nbest_hyps = nbest_hyps[: self.nbest]
+
+ results = []
+ b, n, d = encoder_out.size()
+ for i in range(b):
+
+ 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
+ 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}
+ results.append(result_i)
+
+ 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
+
+ return results, meta_data
+
--
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