From f4f545b7243435116f3cedc4f42cb39bfed3331e Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 30 四月 2024 00:06:43 +0800
Subject: [PATCH] batch
---
funasr/models/sense_voice/model.py | 235 +++++++++++++++++++++++++++++
funasr/models/sense_voice/decoder.py | 202 +++++++++++++++++++++++++
2 files changed, 437 insertions(+), 0 deletions(-)
diff --git a/funasr/models/sense_voice/decoder.py b/funasr/models/sense_voice/decoder.py
index 133508f..4986d50 100644
--- a/funasr/models/sense_voice/decoder.py
+++ b/funasr/models/sense_voice/decoder.py
@@ -335,3 +335,205 @@
x = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
return x
+
+
+class MultiHeadedAttentionSANMDecoder(nn.Module):
+ """Multi-Head Attention layer.
+
+ Args:
+ n_head (int): The number of heads.
+ n_feat (int): The number of features.
+ dropout_rate (float): Dropout rate.
+
+ """
+
+ def __init__(self, n_feat, dropout_rate, kernel_size, sanm_shfit=0):
+ """Construct an MultiHeadedAttention object."""
+ super().__init__()
+
+ self.dropout = nn.Dropout(p=dropout_rate)
+
+ self.fsmn_block = nn.Conv1d(
+ n_feat, n_feat, kernel_size, stride=1, padding=0, groups=n_feat, bias=False
+ )
+ # padding
+ # padding
+ left_padding = (kernel_size - 1) // 2
+ if sanm_shfit > 0:
+ left_padding = left_padding + sanm_shfit
+ right_padding = kernel_size - 1 - left_padding
+ self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0)
+ self.kernel_size = kernel_size
+
+ def forward(self, inputs, mask, cache=None, mask_shfit_chunk=None):
+ """
+ :param x: (#batch, time1, size).
+ :param mask: Mask tensor (#batch, 1, time)
+ :return:
+ """
+ # print("in fsmn, inputs", inputs.size())
+ b, t, d = inputs.size()
+ # logging.info(
+ # "mask: {}".format(mask.size()))
+ if mask is not None:
+ mask = torch.reshape(mask, (b, -1, 1))
+ # logging.info("in fsmn, mask: {}, {}".format(mask.size(), mask[0:100:50, :, :]))
+ if mask_shfit_chunk is not None:
+ # logging.info("in fsmn, mask_fsmn: {}, {}".format(mask_shfit_chunk.size(), mask_shfit_chunk[0:100:50, :, :]))
+ mask = mask * mask_shfit_chunk
+ # logging.info("in fsmn, mask_after_fsmn: {}, {}".format(mask.size(), mask[0:100:50, :, :]))
+ # print("in fsmn, mask", mask.size())
+ # print("in fsmn, inputs", inputs.size())
+ inputs = inputs * mask
+
+ x = inputs.transpose(1, 2)
+ b, d, t = x.size()
+ if cache is None:
+ # print("in fsmn, cache is None, x", x.size())
+
+ x = self.pad_fn(x)
+ if not self.training:
+ cache = x
+ else:
+ # print("in fsmn, cache is not None, x", x.size())
+ # x = torch.cat((x, cache), dim=2)[:, :, :-1]
+ # if t < self.kernel_size:
+ # x = self.pad_fn(x)
+ x = torch.cat((cache[:, :, 1:], x), dim=2)
+ x = x[:, :, -(self.kernel_size + t - 1) :]
+ # print("in fsmn, cache is not None, x_cat", x.size())
+ cache = x
+ x = self.fsmn_block(x)
+ x = x.transpose(1, 2)
+ # print("in fsmn, fsmn_out", x.size())
+ if x.size(1) != inputs.size(1):
+ inputs = inputs[:, -1, :]
+
+ x = x + inputs
+ x = self.dropout(x)
+ if mask is not None:
+ x = x * mask
+ return x, cache
+
+
+class ResidualAttentionBlockFSMN(nn.Module):
+ def __init__(self, n_state: int, n_head: int, cross_attention: bool = False, **kwargs):
+ super().__init__()
+
+ self.attn = MultiHeadedAttentionSANMDecoder(
+ n_state,
+ kwargs.get("self_attention_dropout_rate"),
+ kwargs.get("kernel_size", 20),
+ kwargs.get("sanm_shfit", 10),
+ )
+ self.attn_ln = LayerNorm(n_state)
+
+ self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None
+ self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
+
+ n_mlp = n_state * 4
+ self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
+ self.mlp_ln = LayerNorm(n_state)
+
+ def forward(
+ self,
+ x: Tensor,
+ xa: Optional[Tensor] = None,
+ mask: Optional[Tensor] = None,
+ kv_cache: Optional[dict] = None,
+ **kwargs,
+ ):
+ is_pad_mask = kwargs.get("is_pad_mask", False)
+ is_pad_memory_mask = kwargs.get("is_pad_memory_mask", False)
+ x = x + self.attn(self.attn_ln(x), mask=None, kv_cache=kv_cache, is_pad_mask=is_pad_mask)[0]
+ if self.cross_attn:
+ x = (
+ x
+ + self.cross_attn(
+ self.cross_attn_ln(x), xa, kv_cache=kv_cache, is_pad_mask=is_pad_memory_mask
+ )[0]
+ )
+ x = x + self.mlp(self.mlp_ln(x))
+ return x
+
+
+@tables.register("decoder_classes", "SenseVoiceDecoderFSMN")
+class SenseVoiceDecoderFSMN(nn.Module):
+ def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int, **kwargs):
+ super().__init__()
+
+ self.token_embedding = nn.Embedding(n_vocab, n_state)
+ self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
+
+ self.blocks = nn.ModuleList(
+ [
+ ResidualAttentionBlockFSMN(
+ n_state, n_head, cross_attention=True, layer_id=i, **kwargs
+ )
+ for i in range(n_layer)
+ ]
+ )
+ self.ln = LayerNorm(n_state)
+
+ mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
+ self.register_buffer("mask", mask, persistent=False)
+
+ self.use_padmask = kwargs.get("use_padmask", True)
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ xa: torch.Tensor,
+ kv_cache: Optional[dict] = None,
+ **kwargs,
+ ):
+ """Forward decoder.
+
+ Args:
+ hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
+ hlens: (batch)
+ ys_in_pad:
+ input token ids, int64 (batch, maxlen_out)
+ if input_layer == "embed"
+ input tensor (batch, maxlen_out, #mels) in the other cases
+ ys_in_lens: (batch)
+ Returns:
+ (tuple): tuple containing:
+
+ x: decoded token score before softmax (batch, maxlen_out, token)
+ if use_output_layer is True,
+ olens: (batch, )
+ """
+ # import pdb;pdb.set_trace()
+ use_padmask = self.use_padmask
+ hlens = kwargs.get("hlens", None)
+
+ ys_in_lens = kwargs.get("ys_in_lens", None)
+
+ offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
+ tgt, memory = x, xa
+ tgt[tgt == -1] = 0
+ tgt = self.token_embedding(tgt) + self.positional_embedding[offset : offset + tgt.size(1)]
+ # tgt = self.dropout(tgt)
+
+ x = tgt.to(memory.dtype)
+
+ if use_padmask and hlens is not None:
+ memory_mask = (~make_pad_mask(hlens)[:, None, :]).to(memory.device)
+ else:
+ memory_mask = None
+
+ for layer, block in enumerate(self.blocks):
+ x = block(
+ x,
+ memory,
+ mask=self.mask,
+ memory_mask=memory_mask,
+ is_pad_mask=False,
+ is_pad_memory_mask=True,
+ )
+
+ x = self.ln(x)
+ x = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
+
+ return x
diff --git a/funasr/models/sense_voice/model.py b/funasr/models/sense_voice/model.py
index d06776b..c12107e 100644
--- a/funasr/models/sense_voice/model.py
+++ b/funasr/models/sense_voice/model.py
@@ -476,3 +476,238 @@
results.append(result_i)
return results, meta_data
+
+
+@tables.register("model_classes", "SenseVoiceFSMN")
+class SenseVoiceFSMN(nn.Module):
+ def __init__(self, *args, **kwargs):
+ super().__init__()
+
+ dims = kwargs.get("dims", {})
+ dims = whisper.model.ModelDimensions(**dims)
+ model = whisper.model.Whisper(dims=dims)
+
+ # encoder
+ model.encoder.downsample_rate = kwargs.get("downsample_rate", 4)
+ model.encoder.use_padmask = kwargs.get("use_padmask", True)
+ from .encoder import sense_voice_encode_forward
+
+ model.encoder.forward = types.MethodType(sense_voice_encode_forward, model.encoder)
+
+ # decoder
+ del model.decoder
+ decoder = kwargs.get("decoder", "SenseVoiceDecoder")
+ decoder_conf = kwargs.get("decoder_conf", {})
+ decoder_class = tables.decoder_classes.get(decoder)
+ decoder = decoder_class(
+ vocab_size=dims.n_vocab,
+ encoder_output_size=dims.n_audio_state,
+ **decoder_conf,
+ )
+ model.decoder = 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)
+
+ # import pdb;
+ # pdb.set_trace()
+ if len(text_lengths.size()) > 1:
+ text_lengths = text_lengths[:, 0]
+ if len(speech_lengths.size()) > 1:
+ speech_lengths = speech_lengths[:, 0]
+
+ batch_size, frames, _ = speech.shape
+ _, text_tokens = text.shape
+
+ 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
+ stats["batch_size_x_frames"] = frames * batch_size
+ stats["batch_size_real_frames"] = speech_lengths.sum().item()
+ stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"]
+ stats["batch_size_x_tokens"] = text_tokens * batch_size
+ stats["batch_size_real_tokens"] = text_lengths.sum().item()
+ stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"]
+ stats["batch_size_x_frames_plus_tokens"] = (text_tokens + frames) * 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
+ )
+ # decoder_out, _ = self.model.decoder(encoder_out, encoder_out_lens, ys_pad, 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
--
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