mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
update
This commit is contained in:
游雁
parent
6c358b9a3c
commit
d577dda0ed
@ -1,6 +1,6 @@
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import time
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import torch
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from torch import nn
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from torch import Tensor, nn
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import torch.nn.functional as F
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from typing import Iterable, Optional
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@ -12,6 +12,7 @@ from funasr.train_utils.device_funcs import force_gatherable
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from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
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from funasr.metrics.compute_acc import compute_accuracy, th_accuracy
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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class SinusoidalPositionEncoder(torch.nn.Module):
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@ -905,9 +906,937 @@ class SenseVoiceSmall(nn.Module):
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return results, meta_data
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def export(self, **kwargs):
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from export_meta import export_rebuild_model
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from .export_meta import export_rebuild_model
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if "max_seq_len" not in kwargs:
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kwargs["max_seq_len"] = 512
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models = export_rebuild_model(model=self, **kwargs)
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return models
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class Linear(nn.Linear):
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def forward(self, x: Tensor) -> Tensor:
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return F.linear(
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x,
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self.weight.to(x.dtype),
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None if self.bias is None else self.bias.to(x.dtype),
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)
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class Conv1d(nn.Conv1d):
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def _conv_forward(self, x, weight, bias):
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return super()._conv_forward(
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x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
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)
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def sinusoids(length, channels, max_timescale=10000):
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"""Returns sinusoids for positional embedding"""
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assert channels % 2 == 0
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log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
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inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
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scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
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return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
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class MultiHeadAttention(nn.Module):
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def __init__(self, n_state: int, n_head: int):
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super().__init__()
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self.n_head = n_head
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self.query = Linear(n_state, n_state)
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self.key = Linear(n_state, n_state, bias=False)
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self.value = Linear(n_state, n_state)
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self.out = Linear(n_state, n_state)
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def forward(
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self,
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x: Tensor,
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xa: Optional[Tensor] = None,
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mask: Optional[Tensor] = None,
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kv_cache: Optional[dict] = None,
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**kwargs,
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):
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is_pad_mask = kwargs.get("is_pad_mask", False)
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q = self.query(x)
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if kv_cache is None or xa is None or self.key not in kv_cache:
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# hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
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# otherwise, perform key/value projections for self- or cross-attention as usual.
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k = self.key(x if xa is None else xa)
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v = self.value(x if xa is None else xa)
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else:
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# for cross-attention, calculate keys and values once and reuse in subsequent calls.
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k = kv_cache[self.key]
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v = kv_cache[self.value]
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wv, qk = self.qkv_attention(q, k, v, mask, is_pad_mask=is_pad_mask)
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return self.out(wv), qk
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def qkv_attention(
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self,
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q: Tensor,
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k: Tensor,
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v: Tensor,
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mask: Optional[Tensor] = None,
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**kwargs,
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):
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is_pad_mask = kwargs.get("is_pad_mask", False)
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n_batch, n_ctx, n_state = q.shape
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scale = (n_state // self.n_head) ** -0.25
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q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
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k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
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v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
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qk = q @ k
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if mask is not None:
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if not is_pad_mask:
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qk = qk + mask[:n_ctx, :n_ctx]
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else:
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mask = mask.unsqueeze(1).eq(0) # (batch, 1, t, 1)
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min_value = -float(
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"inf"
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) # min_value = float(np.finfo(torch.tensor(0, dtype=qk.dtype).numpy().dtype).min)
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qk = qk.masked_fill(mask, min_value)
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qk = qk.float()
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w = F.softmax(qk, dim=-1).to(q.dtype)
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if mask is not None and is_pad_mask:
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w = w.masked_fill(mask, 0.0)
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return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
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class MultiHeadAttentionSdpa(nn.Module):
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def __init__(self, n_state: int, n_head: int):
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super().__init__()
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self.n_head = n_head
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self.query = Linear(n_state, n_state)
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self.key = Linear(n_state, n_state, bias=False)
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self.value = Linear(n_state, n_state)
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self.out = Linear(n_state, n_state)
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def forward(
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self,
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x: Tensor,
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xa: Optional[Tensor] = None,
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mask: Optional[Tensor] = None,
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kv_cache: Optional[dict] = None,
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**kwargs,
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):
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is_pad_mask = kwargs.get("is_pad_mask", False)
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q = self.query(x)
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if kv_cache is None or xa is None or self.key not in kv_cache:
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# hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
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# otherwise, perform key/value projections for self- or cross-attention as usual.
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k = self.key(x if xa is None else xa)
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v = self.value(x if xa is None else xa)
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else:
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# for cross-attention, calculate keys and values once and reuse in subsequent calls.
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k = kv_cache[self.key]
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v = kv_cache[self.value]
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wv, qk = self.qkv_attention(q, k, v, mask, is_pad_mask=is_pad_mask, is_causal=False)
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return self.out(wv), qk
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def qkv_attention(
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self,
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q: Tensor,
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k: Tensor,
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v: Tensor,
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mask: Optional[Tensor] = None,
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**kwargs,
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):
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is_pad_mask = kwargs.get("is_pad_mask", False)
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is_causal = kwargs.get("is_causal", False)
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n_batch, n_ctx, n_state = q.shape
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scale = (n_state // self.n_head) ** -0.5
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q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
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k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
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v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
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if mask is not None:
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if not is_pad_mask:
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mask = None
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is_causal = True
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else:
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mask = mask.unsqueeze(1).to(torch.bool) # (batch, 1, 1, t)
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attn_output = torch.nn.functional.scaled_dot_product_attention(
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q,
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k,
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v,
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attn_mask=mask,
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dropout_p=0.0,
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is_causal=is_causal,
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scale=scale,
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)
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if mask is not None:
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attn_output = attn_output.masked_fill(mask.transpose(2, 3).logical_not(), 0.0)
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attn_output = attn_output.transpose(1, 2)
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attn_output = attn_output.flatten(start_dim=2)
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return attn_output, None
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# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral
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class RotaryEmbedding(nn.Module):
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
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super().__init__()
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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inv_freq = 1.0 / (
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self.base
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** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)
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)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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# Build here to make `torch.jit.trace` work.
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self._set_cos_sin_cache(
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seq_len=max_position_embeddings,
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device=self.inv_freq.device,
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dtype=torch.get_default_dtype(),
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)
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(
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self.inv_freq
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)
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freqs = torch.outer(t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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def forward(self, x, seq_len=None):
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# x: [bs, num_attention_heads, seq_len, head_size]
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if seq_len > self.max_seq_len_cached:
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self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
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return (
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self.cos_cached[:seq_len].to(dtype=x.dtype),
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self.sin_cached[:seq_len].to(dtype=x.dtype),
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)
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# Copied from transformers.models.llama.modeling_llama.rotate_half
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`):
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The position indices of the tokens corresponding to the query and key tensors. For example, this can be
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used to pass offsetted position ids when working with a KV-cache.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos[position_ids].unsqueeze(unsqueeze_dim)
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sin = sin[position_ids].unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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class MultiHeadAttentionRoPE(nn.Module):
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def __init__(self, linear_units: int, attention_heads: int, **kwargs):
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super().__init__()
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self.attention_heads = attention_heads
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self.query = Linear(linear_units, linear_units)
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self.key = Linear(linear_units, linear_units, bias=False)
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self.value = Linear(linear_units, linear_units)
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self.out = Linear(linear_units, linear_units)
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self.rotary_emb = RotaryEmbedding(
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attention_heads,
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max_position_embeddings=kwargs.get("max_position_embeddings", 2048),
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base=kwargs.get("rope_theta", 10000),
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)
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def forward(
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self,
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x: Tensor,
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mask: Optional[Tensor] = None,
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**kwargs,
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):
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q = self.query(x)
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k = self.key(x)
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v = self.value(x)
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wv, qk = self.qkv_attention(q, k, v, mask, **kwargs)
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return self.out(wv), qk
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def qkv_attention(
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self,
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q: Tensor,
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k: Tensor,
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v: Tensor,
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mask: Optional[Tensor] = None,
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**kwargs,
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):
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n_batch, n_ctx, n_state = q.shape
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scale = (n_state // self.n_head) ** -0.25
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q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
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k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
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v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
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position_ids = kwargs.get("position_ids", None)
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kv_seq_len = v.shape[-2]
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cos, sin = self.rotary_emb(v, seq_len=kv_seq_len)
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q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids)
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qk = q @ k
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if mask is not None:
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mask = mask.unsqueeze(1).eq(0) # (batch, 1, t, 1)
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min_value = -float(
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"inf"
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) # min_value = float(np.finfo(torch.tensor(0, dtype=qk.dtype).numpy().dtype).min)
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qk = qk.masked_fill(mask, min_value)
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qk = qk.float()
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w = F.softmax(qk, dim=-1).to(q.dtype)
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if mask is not None:
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w = w.masked_fill(mask, 0.0)
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return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
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class MultiHeadAttentionSdpaRoPE(nn.Module):
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def __init__(self, linear_units: int, attention_heads: int, **kwargs):
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super().__init__()
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self.attention_heads = attention_heads
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self.query = Linear(linear_units, linear_units)
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self.key = Linear(linear_units, linear_units, bias=False)
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self.value = Linear(linear_units, linear_units)
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self.out = Linear(linear_units, linear_units)
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self.rotary_emb = RotaryEmbedding(
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attention_heads,
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max_position_embeddings=kwargs.get("max_position_embeddings", 2048),
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base=kwargs.get("rope_theta", 10000),
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)
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def forward(
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self,
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x: Tensor,
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mask: Optional[Tensor] = None,
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**kwargs,
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):
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q = self.query(x)
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k = self.key(x)
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v = self.value(x)
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wv, qk = self.qkv_attention(q, k, v, mask, **kwargs)
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return self.out(wv), qk
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def qkv_attention(
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self,
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q: Tensor,
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k: Tensor,
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v: Tensor,
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mask: Optional[Tensor] = None,
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**kwargs,
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):
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is_causal = kwargs.get("is_causal", False)
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n_batch, n_ctx, n_state = q.shape
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scale = (n_state // self.n_head) ** -0.5
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q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
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k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
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v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
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position_ids = kwargs.get("position_ids", None)
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kv_seq_len = v.shape[-2]
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cos, sin = self.rotary_emb(v, seq_len=kv_seq_len)
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q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids)
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if mask is not None:
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mask = mask.unsqueeze(1).to(torch.bool) # (batch, 1, 1, t)
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attn_output = torch.nn.functional.scaled_dot_product_attention(
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q,
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k,
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v,
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attn_mask=mask,
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dropout_p=0.0,
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is_causal=is_causal,
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scale=scale,
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)
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if mask is not None:
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attn_output = attn_output.masked_fill(mask.transpose(2, 3).logical_not(), 0.0)
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attn_output = attn_output.transpose(1, 2)
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attn_output = attn_output.flatten(start_dim=2)
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return attn_output, None
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class MultiHeadAttentionFSMNRoPE(nn.Module):
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def __init__(self, linear_units: int, attention_heads: int, **kwargs):
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super().__init__()
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self.attention_heads = attention_heads
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self.query = Linear(linear_units, linear_units)
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self.key = Linear(linear_units, linear_units, bias=False)
|
||||
self.value = Linear(linear_units, linear_units)
|
||||
self.out = Linear(linear_units, linear_units)
|
||||
self.rotary_emb = RotaryEmbedding(
|
||||
attention_heads,
|
||||
max_position_embeddings=kwargs.get("max_position_embeddings", 2048),
|
||||
base=kwargs.get("rope_theta", 10000),
|
||||
)
|
||||
|
||||
self.fsmn_block = nn.Conv1d(
|
||||
linear_units,
|
||||
linear_units,
|
||||
kwargs.get("kernel_size", 15),
|
||||
stride=1,
|
||||
padding=0,
|
||||
groups=linear_units,
|
||||
bias=False,
|
||||
)
|
||||
# padding
|
||||
left_padding = (kwargs.get("kernel_size", 15) - 1) // 2
|
||||
left_padding = left_padding + kwargs.get("sanm_shfit", 0)
|
||||
right_padding = kwargs.get("kernel_size", 15) - 1 - left_padding
|
||||
self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0)
|
||||
|
||||
def fsmn(self, inputs, mask):
|
||||
b, t, d = inputs.size()
|
||||
if mask is not None:
|
||||
mask = torch.reshape(mask, (b, -1, 1))
|
||||
inputs = inputs * mask
|
||||
|
||||
x = inputs.transpose(1, 2)
|
||||
x = self.pad_fn(x)
|
||||
x = self.fsmn_block(x)
|
||||
x = x.transpose(1, 2) + inputs
|
||||
# x = self.dropout(x)
|
||||
if mask is not None:
|
||||
x = x * mask
|
||||
return x
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
mask: Optional[Tensor] = None,
|
||||
**kwargs,
|
||||
):
|
||||
q = self.query(x)
|
||||
k = self.key(x)
|
||||
v = self.value(x)
|
||||
|
||||
memory = self.fsmn(v, mask=mask)
|
||||
wv, qk = self.qkv_attention(q, k, v, mask, **kwargs)
|
||||
return self.out(wv) + memory, qk
|
||||
|
||||
def qkv_attention(
|
||||
self,
|
||||
q: Tensor,
|
||||
k: Tensor,
|
||||
v: Tensor,
|
||||
mask: Optional[Tensor] = None,
|
||||
**kwargs,
|
||||
):
|
||||
|
||||
n_batch, n_ctx, n_state = q.shape
|
||||
scale = (n_state // self.n_head) ** -0.25
|
||||
q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
|
||||
k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
|
||||
v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
|
||||
|
||||
position_ids = kwargs.get("position_ids", None)
|
||||
kv_seq_len = v.shape[-2]
|
||||
cos, sin = self.rotary_emb(v, seq_len=kv_seq_len)
|
||||
q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids)
|
||||
|
||||
qk = q @ k
|
||||
if mask is not None:
|
||||
mask = mask.unsqueeze(1).eq(0) # (batch, 1, t, 1)
|
||||
min_value = -float(
|
||||
"inf"
|
||||
) # min_value = float(np.finfo(torch.tensor(0, dtype=qk.dtype).numpy().dtype).min)
|
||||
qk = qk.masked_fill(mask, min_value)
|
||||
|
||||
qk = qk.float()
|
||||
|
||||
w = F.softmax(qk, dim=-1).to(q.dtype)
|
||||
if mask is not None:
|
||||
w = w.masked_fill(mask, 0.0)
|
||||
|
||||
return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
|
||||
|
||||
|
||||
class MultiHeadAttentionFSMNSdpaRoPE(nn.Module):
|
||||
def __init__(self, linear_units: int, attention_heads: int, **kwargs):
|
||||
super().__init__()
|
||||
|
||||
self.attention_heads = attention_heads
|
||||
self.query = Linear(linear_units, linear_units)
|
||||
self.key = Linear(linear_units, linear_units, bias=False)
|
||||
self.value = Linear(linear_units, linear_units)
|
||||
self.out = Linear(linear_units, linear_units)
|
||||
self.rotary_emb = RotaryEmbedding(
|
||||
attention_heads,
|
||||
max_position_embeddings=kwargs.get("max_position_embeddings", 2048),
|
||||
base=kwargs.get("rope_theta", 10000),
|
||||
)
|
||||
|
||||
self.fsmn_block = nn.Conv1d(
|
||||
linear_units,
|
||||
linear_units,
|
||||
kwargs.get("kernel_size", 15),
|
||||
stride=1,
|
||||
padding=0,
|
||||
groups=linear_units,
|
||||
bias=False,
|
||||
)
|
||||
# padding
|
||||
left_padding = (kwargs.get("kernel_size", 15) - 1) // 2
|
||||
left_padding = left_padding + kwargs.get("sanm_shfit", 0)
|
||||
right_padding = kwargs.get("kernel_size", 15) - 1 - left_padding
|
||||
self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0)
|
||||
|
||||
def fsmn(self, inputs, mask):
|
||||
b, t, d = inputs.size()
|
||||
if mask is not None:
|
||||
mask = torch.reshape(mask, (b, -1, 1))
|
||||
inputs = inputs * mask
|
||||
|
||||
x = inputs.transpose(1, 2)
|
||||
x = self.pad_fn(x)
|
||||
x = self.fsmn_block(x)
|
||||
x = x.transpose(1, 2) + inputs
|
||||
# x = self.dropout(x)
|
||||
if mask is not None:
|
||||
x = x * mask
|
||||
return x
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
mask: Optional[Tensor] = None,
|
||||
**kwargs,
|
||||
):
|
||||
|
||||
q = self.query(x)
|
||||
k = self.key(x)
|
||||
v = self.value(x)
|
||||
memory = self.fsmn(v, mask=mask)
|
||||
|
||||
wv, qk = self.qkv_attention(q, k, v, mask, **kwargs)
|
||||
return self.out(wv) + memory, qk
|
||||
|
||||
def qkv_attention(
|
||||
self,
|
||||
q: Tensor,
|
||||
k: Tensor,
|
||||
v: Tensor,
|
||||
mask: Optional[Tensor] = None,
|
||||
**kwargs,
|
||||
):
|
||||
is_causal = kwargs.get("is_causal", False)
|
||||
n_batch, n_ctx, n_state = q.shape
|
||||
scale = (n_state // self.n_head) ** -0.5
|
||||
q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
|
||||
k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
|
||||
v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
|
||||
|
||||
position_ids = kwargs.get("position_ids", None)
|
||||
kv_seq_len = v.shape[-2]
|
||||
cos, sin = self.rotary_emb(v, seq_len=kv_seq_len)
|
||||
q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids)
|
||||
|
||||
if mask is not None:
|
||||
mask = mask.unsqueeze(1).to(torch.bool) # (batch, 1, 1, t)
|
||||
|
||||
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
||||
q,
|
||||
k,
|
||||
v,
|
||||
attn_mask=mask,
|
||||
dropout_p=0.0,
|
||||
is_causal=is_causal,
|
||||
scale=scale,
|
||||
)
|
||||
if mask is not None:
|
||||
attn_output = attn_output.masked_fill(mask.transpose(2, 3).logical_not(), 0.0)
|
||||
attn_output = attn_output.transpose(1, 2)
|
||||
attn_output = attn_output.flatten(start_dim=2)
|
||||
return attn_output, None
|
||||
|
||||
|
||||
att_type_dict = {
|
||||
"default": MultiHeadAttention,
|
||||
"sdpa": MultiHeadAttentionSdpa,
|
||||
"self_att": MultiHeadAttentionRoPE,
|
||||
"self_att_sdpa": MultiHeadAttentionSdpaRoPE,
|
||||
"self_att_fsmn": MultiHeadAttentionFSMNRoPE,
|
||||
"self_att_fsmn_sdpa": MultiHeadAttentionFSMNSdpaRoPE,
|
||||
}
|
||||
|
||||
|
||||
class EncoderLayerSANMLarge(nn.Module):
|
||||
def __init__(self, linear_units: int, attention_heads: int, **kwargs):
|
||||
super().__init__()
|
||||
|
||||
att_type = kwargs.get("att_type", "self_att_fsmn_sdpa")
|
||||
self.attn = att_type_dict[att_type](linear_units, attention_heads)
|
||||
self.attn_ln = LayerNorm(linear_units)
|
||||
|
||||
n_mlp = linear_units * 4
|
||||
self.mlp = nn.Sequential(
|
||||
Linear(linear_units, n_mlp), nn.GELU(), Linear(n_mlp, linear_units)
|
||||
)
|
||||
self.mlp_ln = LayerNorm(linear_units)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
mask: Optional[Tensor] = None,
|
||||
**kwargs,
|
||||
):
|
||||
is_pad_mask = kwargs.get("is_pad_mask", False)
|
||||
|
||||
x = x + self.attn(self.attn_ln(x), mask=mask, is_pad_mask=is_pad_mask)[0]
|
||||
|
||||
x = x + self.mlp(self.mlp_ln(x))
|
||||
return x
|
||||
|
||||
|
||||
@tables.register("encoder_classes", "SenseVoiceEncoder")
|
||||
class SenseVoiceEncoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
input_size,
|
||||
n_ctx: int,
|
||||
linear_units: int,
|
||||
attention_heads: int,
|
||||
num_blocks: int,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__()
|
||||
self.conv1 = Conv1d(input_size, linear_units, kernel_size=3, stride=2, padding=1)
|
||||
self.conv2 = Conv1d(linear_units, linear_units, kernel_size=3, stride=2, padding=1)
|
||||
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
EncoderLayerSANMLarge(
|
||||
linear_units, attention_heads, att_type=kwargs.get("att_type", "default")
|
||||
)
|
||||
for _ in range(num_blocks)
|
||||
]
|
||||
)
|
||||
self.ln_post = LayerNorm(linear_units)
|
||||
self.use_padmask = kwargs.get("use_padmask", True)
|
||||
self.downsample_rate = kwargs.get("downsample_rate", 4)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
ilens: torch.Tensor = None,
|
||||
**kwargs,
|
||||
):
|
||||
use_padmask = self.use_padmask
|
||||
x = F.gelu(self.conv1(x))
|
||||
x = F.gelu(self.conv2(x))
|
||||
x = x.permute(0, 2, 1)
|
||||
|
||||
n_frames = x.size(1)
|
||||
max_pos = n_frames
|
||||
# max_pos = self.positional_embedding.size(0)
|
||||
# max_pos = n_frames if n_frames < max_pos else max_pos
|
||||
# x = (x[:, :max_pos, :] + self.positional_embedding[None, :max_pos, :]).to(x.dtype)
|
||||
|
||||
if ilens is not None:
|
||||
if self.downsample_rate == 4:
|
||||
olens = (
|
||||
1
|
||||
+ (ilens - self.conv1.kernel_size[0] + 2 * self.conv1.padding[0])
|
||||
// self.conv1.stride[0]
|
||||
)
|
||||
else:
|
||||
olens = ilens
|
||||
olens = (
|
||||
1
|
||||
+ (olens - self.conv2.kernel_size[0] + 2 * self.conv2.padding[0])
|
||||
// self.conv2.stride[0]
|
||||
)
|
||||
olens = torch.clamp(olens, max=max_pos)
|
||||
else:
|
||||
olens = None
|
||||
|
||||
if use_padmask and olens is not None:
|
||||
padding_mask = (~make_pad_mask(olens)[:, None, :]).to(torch.bool).to(x.device)
|
||||
else:
|
||||
padding_mask = None
|
||||
|
||||
for layer, block in enumerate(self.blocks):
|
||||
x = block(x, mask=padding_mask, is_pad_mask=True)
|
||||
|
||||
x = self.ln_post(x)
|
||||
|
||||
if ilens is None:
|
||||
return x
|
||||
else:
|
||||
return x, olens
|
||||
|
||||
|
||||
import types
|
||||
import time
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import Tensor
|
||||
from torch import nn
|
||||
from torch.cuda.amp import autocast
|
||||
from funasr.metrics.compute_acc import compute_accuracy, th_accuracy
|
||||
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
|
||||
from funasr.train_utils.device_funcs import force_gatherable
|
||||
from . import whisper_lib as whisper
|
||||
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
|
||||
from funasr.utils.datadir_writer import DatadirWriter
|
||||
|
||||
|
||||
@tables.register("model_classes", "SenseVoiceL")
|
||||
class SenseVoiceL(nn.Module):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__()
|
||||
|
||||
encoder = kwargs.get("kwargs")
|
||||
encoder_conf = kwargs.get("encoder_conf", {})
|
||||
encoder_class = tables.encoder_classes.get(encoder)
|
||||
encoder = encoder_class(**encoder_conf)
|
||||
encoder_output_size = encoder.output_size()
|
||||
|
||||
dims = kwargs.get("dims", {})
|
||||
dims = whisper.model.ModelDimensions(**dims)
|
||||
model = whisper.model.Whisper(dims=dims)
|
||||
|
||||
# encoder
|
||||
del model.encoder
|
||||
model.encoder = encoder
|
||||
|
||||
# decoder
|
||||
model.decoder.use_padmask = kwargs.get("use_padmask", True)
|
||||
from .decoder import sense_voice_decode_forward
|
||||
|
||||
model.decoder.forward = types.MethodType(sense_voice_decode_forward, model.decoder)
|
||||
|
||||
self.model = model
|
||||
|
||||
self.encoder_output_size = self.model.dims.n_audio_state
|
||||
|
||||
self.activation_checkpoint = kwargs.get("activation_checkpoint", False)
|
||||
self.ignore_id = kwargs.get("ignore_id", -1)
|
||||
self.vocab_size = kwargs.get("vocab_size", -1)
|
||||
self.length_normalized_loss = kwargs.get("length_normalized_loss", True)
|
||||
self.criterion_att = LabelSmoothingLoss(
|
||||
size=self.vocab_size,
|
||||
padding_idx=self.ignore_id,
|
||||
smoothing=kwargs.get("lsm_weight", 0.0),
|
||||
normalize_length=self.length_normalized_loss,
|
||||
)
|
||||
|
||||
specaug = kwargs.get("specaug", None)
|
||||
if specaug is not None:
|
||||
specaug_class = tables.specaug_classes.get(specaug)
|
||||
specaug = specaug_class(**kwargs.get("specaug_conf", {}))
|
||||
self.specaug = specaug
|
||||
|
||||
def forward(
|
||||
self,
|
||||
speech: torch.Tensor,
|
||||
speech_lengths: torch.Tensor,
|
||||
text: torch.Tensor,
|
||||
text_lengths: torch.Tensor,
|
||||
**kwargs,
|
||||
):
|
||||
target_mask = kwargs.get("target_mask", None)
|
||||
|
||||
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]
|
||||
|
||||
if self.activation_checkpoint:
|
||||
from torch.utils.checkpoint import checkpoint
|
||||
|
||||
encoder_out, encoder_out_lens = checkpoint(
|
||||
self.encode, speech, speech_lengths, use_reentrant=False
|
||||
)
|
||||
else:
|
||||
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
||||
|
||||
loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
|
||||
encoder_out, encoder_out_lens, text, text_lengths, target_mask=target_mask
|
||||
)
|
||||
loss = loss_att
|
||||
stats = {}
|
||||
stats["acc"] = acc_att
|
||||
stats["loss"] = torch.clone(loss.detach())
|
||||
stats["batch_size"] = batch_size
|
||||
|
||||
# force_gatherable: to-device and to-tensor if scalar for DataParallel
|
||||
if self.length_normalized_loss:
|
||||
batch_size = int((text_lengths + 1).sum())
|
||||
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
|
||||
return loss, stats, weight
|
||||
|
||||
def encode(
|
||||
self,
|
||||
speech: torch.Tensor,
|
||||
speech_lengths: torch.Tensor,
|
||||
**kwargs,
|
||||
):
|
||||
"""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)
|
||||
|
||||
# Forward encoder
|
||||
encoder_out, encoder_out_lens = self.model.encoder(speech.permute(0, 2, 1), speech_lengths)
|
||||
|
||||
return encoder_out, encoder_out_lens
|
||||
|
||||
def _calc_att_loss(
|
||||
self,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
ys_pad: torch.Tensor,
|
||||
ys_pad_lens: torch.Tensor,
|
||||
**kwargs,
|
||||
):
|
||||
target_mask = kwargs.get("target_mask", None)
|
||||
stats = {}
|
||||
|
||||
# 1. Forward decoder
|
||||
decoder_out = self.model.decoder(
|
||||
x=ys_pad, xa=encoder_out, hlens=encoder_out_lens, ys_in_lens=ys_pad_lens
|
||||
)
|
||||
|
||||
# 2. Compute attention loss
|
||||
mask = torch.ones_like(ys_pad) * (-1)
|
||||
ys_pad_mask = (ys_pad * target_mask + mask * (1 - target_mask)).to(torch.int64)
|
||||
ys_pad_mask[ys_pad_mask == 0] = -1
|
||||
loss_att = self.criterion_att(decoder_out[:, :-1, :], ys_pad_mask[:, 1:])
|
||||
|
||||
with torch.no_grad():
|
||||
preds = torch.argmax(decoder_out, -1)
|
||||
acc_att = compute_accuracy(
|
||||
preds[:, :-1], ys_pad_mask[:, 1:], ignore_label=self.ignore_id
|
||||
)
|
||||
|
||||
return loss_att, acc_att, None, None
|
||||
|
||||
def inference(
|
||||
self,
|
||||
data_in,
|
||||
data_lengths=None,
|
||||
key: list = None,
|
||||
tokenizer=None,
|
||||
frontend=None,
|
||||
**kwargs,
|
||||
):
|
||||
if kwargs.get("batch_size", 1) > 1:
|
||||
raise NotImplementedError("batch decoding is not implemented")
|
||||
|
||||
if frontend is None and not hasattr(self, "frontend"):
|
||||
frontend_class = tables.frontend_classes.get("WhisperFrontend")
|
||||
frontend = frontend_class(
|
||||
n_mels=self.model.dims.n_mels, do_pad_trim=kwargs.get("do_pad_trim", True)
|
||||
)
|
||||
self.frontend = frontend
|
||||
else:
|
||||
frontend = frontend if frontend is not None else self.frontend
|
||||
|
||||
meta_data = {}
|
||||
if (
|
||||
isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
|
||||
): # fbank
|
||||
speech, speech_lengths = data_in, data_lengths
|
||||
if len(speech.shape) < 3:
|
||||
speech = speech[None, :, :]
|
||||
if speech_lengths is None:
|
||||
speech_lengths = speech.shape[1]
|
||||
else:
|
||||
# extract fbank feats
|
||||
time1 = time.perf_counter()
|
||||
audio_sample_list = load_audio_text_image_video(
|
||||
data_in,
|
||||
fs=frontend.fs if hasattr(frontend, "fs") else 16000,
|
||||
audio_fs=kwargs.get("fs", 16000),
|
||||
data_type=kwargs.get("data_type", "sound"),
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
time2 = time.perf_counter()
|
||||
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
||||
speech, speech_lengths = extract_fbank(
|
||||
audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
|
||||
)
|
||||
time3 = time.perf_counter()
|
||||
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
||||
frame_shift = frontend.frame_shift if hasattr(frontend, "frame_shift") else 10
|
||||
lfr_n = frontend.lfr_n if hasattr(frontend, "lfr_n") else 1
|
||||
meta_data["batch_data_time"] = speech_lengths.sum().item() * frame_shift * lfr_n / 1000
|
||||
|
||||
speech = speech.to(device=kwargs["device"])[0, :, :]
|
||||
speech_lengths = speech_lengths.to(device=kwargs["device"])
|
||||
|
||||
DecodingOptions = kwargs.get("DecodingOptions", {})
|
||||
task = DecodingOptions.get("task", "ASR")
|
||||
if isinstance(task, str):
|
||||
task = [task]
|
||||
task = "".join([f"<|{x}|>" for x in task])
|
||||
initial_prompt = kwargs.get("initial_prompt", f"<|startoftranscript|>{task}")
|
||||
DecodingOptions["initial_prompt"] = initial_prompt
|
||||
|
||||
language = DecodingOptions.get("language", None)
|
||||
language = None if language == "auto" else language
|
||||
DecodingOptions["language"] = language
|
||||
|
||||
DecodingOptions["vocab_path"] = kwargs["tokenizer_conf"].get("vocab_path", None)
|
||||
|
||||
if "without_timestamps" not in DecodingOptions:
|
||||
DecodingOptions["without_timestamps"] = True
|
||||
|
||||
options = whisper.DecodingOptions(**DecodingOptions)
|
||||
|
||||
result = whisper.decode(self.model, speech, options)
|
||||
text = f"{result.text}"
|
||||
results = []
|
||||
result_i = {"key": key[0], "text": text}
|
||||
|
||||
results.append(result_i)
|
||||
|
||||
return results, meta_data
|
||||
|
||||
Loading…
Reference in New Issue
Block a user