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transducer inference
This commit is contained in:
shixian.shi
parent
509bc88903
commit
9bed46a31e
@ -1,238 +0,0 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2019 Shigeki Karita
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Multi-Head Attention layer definition."""
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import math
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import numpy
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import torch
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from torch import nn
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from typing import Optional, Tuple
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import torch.nn.functional as F
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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import funasr.models.lora.layers as lora
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class RelPositionMultiHeadedAttentionChunk(torch.nn.Module):
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"""RelPositionMultiHeadedAttention definition.
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Args:
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num_heads: Number of attention heads.
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embed_size: Embedding size.
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dropout_rate: Dropout rate.
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"""
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def __init__(
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self,
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num_heads: int,
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embed_size: int,
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dropout_rate: float = 0.0,
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simplified_attention_score: bool = False,
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) -> None:
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"""Construct an MultiHeadedAttention object."""
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super().__init__()
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self.d_k = embed_size // num_heads
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self.num_heads = num_heads
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assert self.d_k * num_heads == embed_size, (
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"embed_size (%d) must be divisible by num_heads (%d)",
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(embed_size, num_heads),
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)
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self.linear_q = torch.nn.Linear(embed_size, embed_size)
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self.linear_k = torch.nn.Linear(embed_size, embed_size)
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self.linear_v = torch.nn.Linear(embed_size, embed_size)
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self.linear_out = torch.nn.Linear(embed_size, embed_size)
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if simplified_attention_score:
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self.linear_pos = torch.nn.Linear(embed_size, num_heads)
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self.compute_att_score = self.compute_simplified_attention_score
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else:
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self.linear_pos = torch.nn.Linear(embed_size, embed_size, bias=False)
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self.pos_bias_u = torch.nn.Parameter(torch.Tensor(num_heads, self.d_k))
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self.pos_bias_v = torch.nn.Parameter(torch.Tensor(num_heads, self.d_k))
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torch.nn.init.xavier_uniform_(self.pos_bias_u)
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torch.nn.init.xavier_uniform_(self.pos_bias_v)
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self.compute_att_score = self.compute_attention_score
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self.dropout = torch.nn.Dropout(p=dropout_rate)
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self.attn = None
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def rel_shift(self, x: torch.Tensor, left_context: int = 0) -> torch.Tensor:
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"""Compute relative positional encoding.
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Args:
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x: Input sequence. (B, H, T_1, 2 * T_1 - 1)
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left_context: Number of frames in left context.
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Returns:
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x: Output sequence. (B, H, T_1, T_2)
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"""
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batch_size, n_heads, time1, n = x.shape
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time2 = time1 + left_context
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batch_stride, n_heads_stride, time1_stride, n_stride = x.stride()
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return x.as_strided(
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(batch_size, n_heads, time1, time2),
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(batch_stride, n_heads_stride, time1_stride - n_stride, n_stride),
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storage_offset=(n_stride * (time1 - 1)),
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)
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def compute_simplified_attention_score(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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pos_enc: torch.Tensor,
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left_context: int = 0,
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) -> torch.Tensor:
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"""Simplified attention score computation.
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Reference: https://github.com/k2-fsa/icefall/pull/458
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Args:
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query: Transformed query tensor. (B, H, T_1, d_k)
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key: Transformed key tensor. (B, H, T_2, d_k)
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pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
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left_context: Number of frames in left context.
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Returns:
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: Attention score. (B, H, T_1, T_2)
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"""
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pos_enc = self.linear_pos(pos_enc)
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matrix_ac = torch.matmul(query, key.transpose(2, 3))
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matrix_bd = self.rel_shift(
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pos_enc.transpose(1, 2).unsqueeze(2).repeat(1, 1, query.size(2), 1),
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left_context=left_context,
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)
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return (matrix_ac + matrix_bd) / math.sqrt(self.d_k)
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def compute_attention_score(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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pos_enc: torch.Tensor,
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left_context: int = 0,
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) -> torch.Tensor:
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"""Attention score computation.
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Args:
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query: Transformed query tensor. (B, H, T_1, d_k)
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key: Transformed key tensor. (B, H, T_2, d_k)
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pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
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left_context: Number of frames in left context.
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Returns:
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: Attention score. (B, H, T_1, T_2)
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"""
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p = self.linear_pos(pos_enc).view(pos_enc.size(0), -1, self.num_heads, self.d_k)
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query = query.transpose(1, 2)
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q_with_bias_u = (query + self.pos_bias_u).transpose(1, 2)
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q_with_bias_v = (query + self.pos_bias_v).transpose(1, 2)
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matrix_ac = torch.matmul(q_with_bias_u, key.transpose(-2, -1))
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matrix_bd = torch.matmul(q_with_bias_v, p.permute(0, 2, 3, 1))
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matrix_bd = self.rel_shift(matrix_bd, left_context=left_context)
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return (matrix_ac + matrix_bd) / math.sqrt(self.d_k)
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def forward_qkv(
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self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Transform query, key and value.
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Args:
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query: Query tensor. (B, T_1, size)
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key: Key tensor. (B, T_2, size)
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v: Value tensor. (B, T_2, size)
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Returns:
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q: Transformed query tensor. (B, H, T_1, d_k)
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k: Transformed key tensor. (B, H, T_2, d_k)
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v: Transformed value tensor. (B, H, T_2, d_k)
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"""
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n_batch = query.size(0)
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q = (
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self.linear_q(query)
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.view(n_batch, -1, self.num_heads, self.d_k)
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.transpose(1, 2)
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)
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k = (
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self.linear_k(key)
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.view(n_batch, -1, self.num_heads, self.d_k)
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.transpose(1, 2)
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)
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v = (
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self.linear_v(value)
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.view(n_batch, -1, self.num_heads, self.d_k)
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.transpose(1, 2)
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)
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return q, k, v
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def forward_attention(
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self,
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value: torch.Tensor,
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scores: torch.Tensor,
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mask: torch.Tensor,
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chunk_mask: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""Compute attention context vector.
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Args:
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value: Transformed value. (B, H, T_2, d_k)
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scores: Attention score. (B, H, T_1, T_2)
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mask: Source mask. (B, T_2)
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chunk_mask: Chunk mask. (T_1, T_1)
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Returns:
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attn_output: Transformed value weighted by attention score. (B, T_1, H * d_k)
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"""
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batch_size = scores.size(0)
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mask = mask.unsqueeze(1).unsqueeze(2)
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if chunk_mask is not None:
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mask = chunk_mask.unsqueeze(0).unsqueeze(1) | mask
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scores = scores.masked_fill(mask, float("-inf"))
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self.attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0)
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attn_output = self.dropout(self.attn)
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attn_output = torch.matmul(attn_output, value)
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attn_output = self.linear_out(
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attn_output.transpose(1, 2)
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.contiguous()
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.view(batch_size, -1, self.num_heads * self.d_k)
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)
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return attn_output
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def forward(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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pos_enc: torch.Tensor,
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mask: torch.Tensor,
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chunk_mask: Optional[torch.Tensor] = None,
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left_context: int = 0,
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) -> torch.Tensor:
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"""Compute scaled dot product attention with rel. positional encoding.
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Args:
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query: Query tensor. (B, T_1, size)
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key: Key tensor. (B, T_2, size)
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value: Value tensor. (B, T_2, size)
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pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
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mask: Source mask. (B, T_2)
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chunk_mask: Chunk mask. (T_1, T_1)
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left_context: Number of frames in left context.
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Returns:
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: Output tensor. (B, T_1, H * d_k)
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"""
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q, k, v = self.forward_qkv(query, key, value)
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scores = self.compute_att_score(q, k, pos_enc, left_context=left_context)
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return self.forward_attention(v, scores, mask, chunk_mask=chunk_mask)
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@ -1,220 +0,0 @@
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# import torch
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# from torch import nn
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# from torch import Tensor
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# import logging
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# import numpy as np
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# from funasr.train_utils.device_funcs import to_device
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# from funasr.models.transformer.utils.nets_utils import make_pad_mask
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# from funasr.models.scama.utils import sequence_mask
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# from typing import Optional, Tuple
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#
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# from funasr.register import tables
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#
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# class mae_loss(nn.Module):
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#
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# def __init__(self, normalize_length=False):
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# super(mae_loss, self).__init__()
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# self.normalize_length = normalize_length
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# self.criterion = torch.nn.L1Loss(reduction='sum')
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#
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# def forward(self, token_length, pre_token_length):
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# loss_token_normalizer = token_length.size(0)
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# if self.normalize_length:
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# loss_token_normalizer = token_length.sum().type(torch.float32)
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# loss = self.criterion(token_length, pre_token_length)
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# loss = loss / loss_token_normalizer
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# return loss
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#
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#
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# def cif(hidden, alphas, threshold):
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# batch_size, len_time, hidden_size = hidden.size()
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#
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# # loop varss
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# integrate = torch.zeros([batch_size], device=hidden.device)
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# frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
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# # intermediate vars along time
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# list_fires = []
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# list_frames = []
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#
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# for t in range(len_time):
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# alpha = alphas[:, t]
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# distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
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#
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# integrate += alpha
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# list_fires.append(integrate)
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#
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# fire_place = integrate >= threshold
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# integrate = torch.where(fire_place,
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# integrate - torch.ones([batch_size], device=hidden.device),
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# integrate)
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# cur = torch.where(fire_place,
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# distribution_completion,
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# alpha)
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# remainds = alpha - cur
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#
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# frame += cur[:, None] * hidden[:, t, :]
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# list_frames.append(frame)
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# frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
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# remainds[:, None] * hidden[:, t, :],
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# frame)
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#
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# fires = torch.stack(list_fires, 1)
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# frames = torch.stack(list_frames, 1)
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# list_ls = []
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# len_labels = torch.round(alphas.sum(-1)).int()
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# max_label_len = len_labels.max()
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# for b in range(batch_size):
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# fire = fires[b, :]
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# l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
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# pad_l = torch.zeros([max_label_len - l.size(0), hidden_size], device=hidden.device)
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# list_ls.append(torch.cat([l, pad_l], 0))
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# return torch.stack(list_ls, 0), fires
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#
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#
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# def cif_wo_hidden(alphas, threshold):
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# batch_size, len_time = alphas.size()
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#
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# # loop varss
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# integrate = torch.zeros([batch_size], device=alphas.device)
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# # intermediate vars along time
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# list_fires = []
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#
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# for t in range(len_time):
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# alpha = alphas[:, t]
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#
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# integrate += alpha
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# list_fires.append(integrate)
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#
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# fire_place = integrate >= threshold
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# integrate = torch.where(fire_place,
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# integrate - torch.ones([batch_size], device=alphas.device)*threshold,
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# integrate)
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#
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# fires = torch.stack(list_fires, 1)
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# return fires
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#
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# @tables.register("predictor_classes", "BATPredictor")
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# class BATPredictor(nn.Module):
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# def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0, return_accum=False):
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# super(BATPredictor, self).__init__()
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#
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# self.pad = nn.ConstantPad1d((l_order, r_order), 0)
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# self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim)
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# self.cif_output = nn.Linear(idim, 1)
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# self.dropout = torch.nn.Dropout(p=dropout)
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# self.threshold = threshold
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# self.smooth_factor = smooth_factor
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# self.noise_threshold = noise_threshold
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# self.return_accum = return_accum
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#
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# def cif(
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# self,
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# input: Tensor,
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# alpha: Tensor,
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# beta: float = 1.0,
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# return_accum: bool = False,
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# ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
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# B, S, C = input.size()
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# assert tuple(alpha.size()) == (B, S), f"{alpha.size()} != {(B, S)}"
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#
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# dtype = alpha.dtype
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# alpha = alpha.float()
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#
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# alpha_sum = alpha.sum(1)
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# feat_lengths = (alpha_sum / beta).floor().long()
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# T = feat_lengths.max()
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#
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# # aggregate and integrate
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# csum = alpha.cumsum(-1)
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# with torch.no_grad():
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# # indices used for scattering
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# right_idx = (csum / beta).floor().long().clip(max=T)
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# left_idx = right_idx.roll(1, dims=1)
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# left_idx[:, 0] = 0
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#
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# # count # of fires from each source
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# fire_num = right_idx - left_idx
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# extra_weights = (fire_num - 1).clip(min=0)
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# # The extra entry in last dim is for
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# output = input.new_zeros((B, T + 1, C))
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# source_range = torch.arange(1, 1 + S).unsqueeze(0).type_as(input)
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# zero = alpha.new_zeros((1,))
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#
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# # right scatter
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# fire_mask = fire_num > 0
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# right_weight = torch.where(
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# fire_mask,
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# csum - right_idx.type_as(alpha) * beta,
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# zero
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# ).type_as(input)
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# # assert right_weight.ge(0).all(), f"{right_weight} should be non-negative."
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# output.scatter_add_(
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# 1,
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# right_idx.unsqueeze(-1).expand(-1, -1, C),
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# right_weight.unsqueeze(-1) * input
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# )
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#
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# # left scatter
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# left_weight = (
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# alpha - right_weight - extra_weights.type_as(alpha) * beta
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# ).type_as(input)
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# output.scatter_add_(
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# 1,
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# left_idx.unsqueeze(-1).expand(-1, -1, C),
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# left_weight.unsqueeze(-1) * input
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# )
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#
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# # extra scatters
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# if extra_weights.ge(0).any():
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# extra_steps = extra_weights.max().item()
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# tgt_idx = left_idx
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# src_feats = input * beta
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# for _ in range(extra_steps):
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# tgt_idx = (tgt_idx + 1).clip(max=T)
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# # (B, S, 1)
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# src_mask = (extra_weights > 0)
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# output.scatter_add_(
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# 1,
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# tgt_idx.unsqueeze(-1).expand(-1, -1, C),
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# src_feats * src_mask.unsqueeze(2)
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# )
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# extra_weights -= 1
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#
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# output = output[:, :T, :]
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#
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# if return_accum:
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# return output, csum
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# else:
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# return output, alpha
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#
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# def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None, target_label_length=None):
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# h = hidden
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# context = h.transpose(1, 2)
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# queries = self.pad(context)
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# memory = self.cif_conv1d(queries)
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# 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
|
||||
@ -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
|
||||
@ -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.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.criterion_att = LabelSmoothingLoss(
|
||||
size=vocab_size,
|
||||
padding_idx=ignore_id,
|
||||
smoothing=lsm_weight,
|
||||
normalize_length=length_normalized_loss,
|
||||
)
|
||||
|
||||
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 @@ class BATModel(nn.Module):
|
||||
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 @@ class BATModel(nn.Module):
|
||||
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 @@ class BATModel(nn.Module):
|
||||
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 @@ class BATModel(nn.Module):
|
||||
reduction="sum",
|
||||
)
|
||||
loss_ctc /= target.size(0)
|
||||
|
||||
|
||||
return loss_ctc
|
||||
|
||||
|
||||
def _calc_lm_loss(
|
||||
self,
|
||||
decoder_out: torch.Tensor,
|
||||
@ -456,17 +384,17 @@ class BATModel(nn.Module):
|
||||
"""
|
||||
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 @@ class BATModel(nn.Module):
|
||||
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
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user