Revert "shfit to shift (#2266)" (#2336)

zhifu gao

2024-12-25 3f8294b9d7deaa0cbdb0b2ef6f3802d46ae133a9

Revert "shfit to shift (#2266)" (#2336)

This reverts commit 1367973f9818d8e15c7bf52ad6ffba4ddb6ac2b2.

 docs/tutorial/Tables.md

@@ -5,13 +5,13 @@
The original intention of the funasr-1.x.x version is to make model integration easier. The core feature is the registry and AutoModel:

*   The introduction of the registry enables the development of building blocks to access the model, compatible with a variety of tasks;

    
*   The newly designed AutoModel interface unifies modelscope, huggingface, and funasr inference and training interfaces, and supports free download of repositories;

    
*   Support model export, demo-level service deployment, and industrial-level multi-concurrent service deployment;

    
*   Unify academic and industrial model inference training scripts;

    

# Quick to get started

@@ -51,19 +51,19 @@
```

*   `model`(str): [Model Warehouse](https://github.com/alibaba-damo-academy/FunASR/tree/main/model_zoo)The model name in, or the model path in the local disk

    
*   `device`(str): `cuda:0`(Default gpu0), using GPU for inference, specified. If`cpu`Then the CPU is used for inference

    
*   `ncpu`(int): `4`(Default), set the number of threads used for CPU internal operation parallelism

    
*   `output_dir`(str): `None`(Default) If set, the output path of the output result

    
*   `batch_size`(int): `1`(Default), batch processing during decoding, number of samples

    
*   `hub`(str)：`ms`(Default) to download the model from modelscope. If`hf`To download the model from huggingface.

    
*   `**kwargs`(dict): All in`config.yaml`Parameters, which can be specified directly here, for example, the maximum cut length in the vad model.`max_single_segment_time=6000`(Milliseconds).

    

#### AutoModel reasoning

@@ -72,13 +72,13 @@
```

*   *   wav file path, for example: asr\_example.wav

        
    *   pcm file path, for example: asr\_example.pcm, you need to specify the audio sampling rate fs (default is 16000)

        
    *   Audio byte stream, for example: microphone byte data

        
    *   wav.scp,kaldi-style wav list (`wav_id \t wav_path`), for example:

        

```plaintext
Asr_example1./audios/asr_example1.wav
@@ -89,13 +89,13 @@
In this input

*   Audio sampling points, for example:`audio, rate = soundfile.read("asr_example_zh.wav")`Is numpy.ndarray. batch input is supported. The type is list:`[audio_sample1, audio_sample2, ..., audio_sampleN]`

    
*   fbank input, support group batch. shape is \[batch, frames, dim\], type is torch.Tensor, for example

    
*   `output_dir`: None (default), if set, the output path of the output result

    
*   `**kwargs`(dict): Model-related inference parameters, e.g,`beam_size=10`,`decoding_ctc_weight=0.1`.

    

Detailed documentation link:[https://github.com/modelscope/FunASR/blob/main/examples/README\_zh.md](https://github.com/modelscope/FunASR/blob/main/examples/README_zh.md)

@@ -128,7 +128,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm


@@ -208,10 +208,10 @@
  "model": {"type" : "funasr"},
  "pipeline": {"type":"funasr-pipeline"},
  "model_name_in_hub": {
    "ms":"",
    "ms":"", 
    "hf":""},
  "file_path_metas": {
    "init_param":"model.pt",
    "init_param":"model.pt", 
    "config":"config.yaml",
    "tokenizer_conf": {"bpemodel": "chn_jpn_yue_eng_ko_spectok.bpe.model"},
    "frontend_conf":{"cmvn_file": "am.mvn"}}
@@ -274,7 +274,7 @@
  def forward(
      self,
      **kwargs,
  ):
  ):  

  def inference(
      self,
@@ -320,9 +320,9 @@
## Principles of Registration

*   Model: models are independent of each other. Each Model needs to create a new Model directory under funasr/models/. Do not use class inheritance method!!! Do not import from other model directories, and put everything you need into your own model directory!!! Do not modify the existing model code!!!

    
*   dataset,frontend,tokenizer, if you can reuse the existing one, reuse it directly, if you cannot reuse it, please register a new one, modify it again, and do not modify the original one!!!

    

# Independent warehouse

@@ -337,7 +337,7 @@
model = AutoModel (
model="iic/SenseVoiceSmall ",
trust_remote_code=True
remote_code = "./model.py",
remote_code = "./model.py", 
)
```

@@ -360,4 +360,4 @@
print(text)
```

Trim reference:[https://github.com/FunAudioLLM/SenseVoice/blob/main/finetune.sh](https://github.com/FunAudioLLM/SenseVoice/blob/main/finetune.sh)
Trim reference:[https://github.com/FunAudioLLM/SenseVoice/blob/main/finetune.sh](https://github.com/FunAudioLLM/SenseVoice/blob/main/finetune.sh)

 docs/tutorial/Tables_zh.md

@@ -5,13 +5,13 @@
funasr-1.x.x 版本的设计初衷是【**让模型集成更简单**】，核心feature为注册表与AutoModel：

*   注册表的引入，使得开发中可以用搭积木的方式接入模型，兼容多种task；

    
*   新设计的AutoModel接口，统一modelscope、huggingface与funasr推理与训练接口，支持自由选择下载仓库；

    
*   支持模型导出，demo级别服务部署，以及工业级别多并发服务部署；

    
*   统一学术与工业模型推理训练脚本；

    

# 快速上手

@@ -51,19 +51,19 @@
```

*   `model`(str): [模型仓库](https://github.com/alibaba-damo-academy/FunASR/tree/main/model_zoo) 中的模型名称，或本地磁盘中的模型路径

    
*   `device`(str): `cuda:0`（默认gpu0），使用 GPU 进行推理，指定。如果为`cpu`，则使用 CPU 进行推理

    
*   `ncpu`(int): `4` （默认），设置用于 CPU 内部操作并行性的线程数

    
*   `output_dir`(str): `None` （默认），如果设置，输出结果的输出路径

    
*   `batch_size`(int): `1` （默认），解码时的批处理，样本个数

    
*   `hub`(str)：`ms`（默认），从modelscope下载模型。如果为`hf`，从huggingface下载模型。

    
*   `**kwargs`(dict): 所有在`config.yaml`中参数，均可以直接在此处指定，例如，vad模型中最大切割长度 `max_single_segment_time=6000` （毫秒）。

    

#### AutoModel 推理

@@ -72,13 +72,13 @@
```

*   *   wav文件路径, 例如: asr\_example.wav

        
    *   pcm文件路径, 例如: asr\_example.pcm，此时需要指定音频采样率fs（默认为16000）

        
    *   音频字节数流，例如：麦克风的字节数数据

        
    *   wav.scp，kaldi 样式的 wav 列表 (`wav_id \t wav_path`), 例如:

        

```plaintext
asr_example1  ./audios/asr_example1.wav
@@ -89,13 +89,13 @@
在这种输入 

*   音频采样点，例如：`audio, rate = soundfile.read("asr_example_zh.wav")`, 数据类型为 numpy.ndarray。支持batch输入，类型为list： `[audio_sample1, audio_sample2, ..., audio_sampleN]`

    
*   fbank输入，支持组batch。shape为\[batch, frames, dim\]，类型为torch.Tensor，例如

    
*   `output_dir`: None （默认），如果设置，输出结果的输出路径

    
*   `**kwargs`(dict): 与模型相关的推理参数，例如，`beam_size=10`，`decoding_ctc_weight=0.1`。

    

详细文档链接：[https://github.com/modelscope/FunASR/blob/main/examples/README\_zh.md](https://github.com/modelscope/FunASR/blob/main/examples/README_zh.md)

@@ -128,7 +128,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm


@@ -208,10 +208,10 @@
  "model": {"type" : "funasr"},
  "pipeline": {"type":"funasr-pipeline"},
  "model_name_in_hub": {
    "ms":"",
    "ms":"", 
    "hf":""},
  "file_path_metas": {
    "init_param":"model.pt",
    "init_param":"model.pt", 
    "config":"config.yaml",
    "tokenizer_conf": {"bpemodel": "chn_jpn_yue_eng_ko_spectok.bpe.model"},
    "frontend_conf":{"cmvn_file": "am.mvn"}}
@@ -274,7 +274,7 @@
  def forward(
      self,
      **kwargs,
  ):
  ):  

  def inference(
      self,
@@ -320,9 +320,9 @@
## 注册原则

*   Model：模型之间互相独立，每一个模型，都需要在funasr/models/下面新建一个模型目录，不要采用类的继承方法！！！不要从其他模型目录中import，所有需要用到的都单独放到自己的模型目录中！！！不要修改现有的模型代码！！！

    
*   dataset，frontend，tokenizer，如果能复用现有的，直接复用，如果不能复用，请注册一个新的，再修改，不要修改原来的！！！

    

# 独立仓库

@@ -336,8 +336,8 @@
# trust_remote_code：`True` 表示 model 代码实现从 `remote_code` 处加载，`remote_code` 指定 `model` 具体代码的位置（例如，当前目录下的 `model.py`），支持绝对路径与相对路径，以及网络 url。
model = AutoModel(
    model="iic/SenseVoiceSmall",
    trust_remote_code=True,
    remote_code="./model.py",
    trust_remote_code=True,  
    remote_code="./model.py", 
)
```

@@ -360,4 +360,4 @@
print(text)
```

微调参考：[https://github.com/FunAudioLLM/SenseVoice/blob/main/finetune.sh](https://github.com/FunAudioLLM/SenseVoice/blob/main/finetune.sh)
微调参考：[https://github.com/FunAudioLLM/SenseVoice/blob/main/finetune.sh](https://github.com/FunAudioLLM/SenseVoice/blob/main/finetune.sh)

 examples/industrial_data_pretraining/sanm_kws/conf/sanm_6e_320_256_fdim40_t2602.yaml

@@ -18,7 +18,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm

# frontend related

 examples/industrial_data_pretraining/sanm_kws_streaming/conf/sanm_6e_320_256_fdim40_t2602.yaml

@@ -18,7 +18,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm
    chunk_size:
    - 16

 funasr/models/bicif_paraformer/template.yaml

@@ -30,7 +30,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm

# decoder
@@ -45,7 +45,7 @@
    src_attention_dropout_rate: 0.1
    att_layer_num: 16
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0

predictor: CifPredictorV3
predictor_conf:

 funasr/models/contextual_paraformer/decoder.py

@@ -137,7 +137,7 @@
        concat_after: bool = False,
        att_layer_num: int = 6,
        kernel_size: int = 21,
        sanm_shift: int = 0,
        sanm_shfit: int = 0,
    ):
        super().__init__(
            vocab_size=vocab_size,
@@ -179,14 +179,14 @@

        self.att_layer_num = att_layer_num
        self.num_blocks = num_blocks
        if sanm_shift is None:
            sanm_shift = (kernel_size - 1) // 2
        if sanm_shfit is None:
            sanm_shfit = (kernel_size - 1) // 2
        self.decoders = repeat(
            att_layer_num - 1,
            lambda lnum: DecoderLayerSANM(
                attention_dim,
                MultiHeadedAttentionSANMDecoder(
                    attention_dim, self_attention_dropout_rate, kernel_size, sanm_shift=sanm_shift
                    attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
                ),
                MultiHeadedAttentionCrossAtt(
                    attention_heads, attention_dim, src_attention_dropout_rate
@@ -210,7 +210,7 @@
        self.last_decoder = ContextualDecoderLayer(
            attention_dim,
            MultiHeadedAttentionSANMDecoder(
                attention_dim, self_attention_dropout_rate, kernel_size, sanm_shift=sanm_shift
                attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
            ),
            MultiHeadedAttentionCrossAtt(
                attention_heads, attention_dim, src_attention_dropout_rate
@@ -228,7 +228,7 @@
                lambda lnum: DecoderLayerSANM(
                    attention_dim,
                    MultiHeadedAttentionSANMDecoder(
                        attention_dim, self_attention_dropout_rate, kernel_size, sanm_shift=0
                        attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=0
                    ),
                    None,
                    PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),

 funasr/models/contextual_paraformer/template.yaml

@@ -30,7 +30,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm


@@ -46,7 +46,7 @@
    src_attention_dropout_rate: 0.1
    att_layer_num: 16
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0

predictor: CifPredictorV2
predictor_conf:
@@ -126,4 +126,4 @@
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: true
normalize: null
normalize: null

 funasr/models/ct_transformer/template.yaml

@@ -41,7 +41,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm
    padding_idx: 0


 funasr/models/ct_transformer_streaming/attention.py

@@ -11,9 +11,9 @@
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def forward(self, x, mask, mask_shift_chunk=None, mask_att_chunk_encoder=None):
    def forward(self, x, mask, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
        q_h, k_h, v_h, v = self.forward_qkv(x)
        fsmn_memory = self.forward_fsmn(v, mask[0], mask_shift_chunk)
        fsmn_memory = self.forward_fsmn(v, mask[0], mask_shfit_chunk)
        q_h = q_h * self.d_k ** (-0.5)
        scores = torch.matmul(q_h, k_h.transpose(-2, -1))
        att_outs = self.forward_attention(v_h, scores, mask[1], mask_att_chunk_encoder)

 funasr/models/ct_transformer_streaming/encoder.py

@@ -56,7 +56,7 @@
        self.stochastic_depth_rate = stochastic_depth_rate
        self.dropout_rate = dropout_rate

    def forward(self, x, mask, cache=None, mask_shift_chunk=None, mask_att_chunk_encoder=None):
    def forward(self, x, mask, cache=None, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
        """Compute encoded features.

        Args:
@@ -93,7 +93,7 @@
                    self.self_attn(
                        x,
                        mask,
                        mask_shift_chunk=mask_shift_chunk,
                        mask_shfit_chunk=mask_shfit_chunk,
                        mask_att_chunk_encoder=mask_att_chunk_encoder,
                    ),
                ),
@@ -109,7 +109,7 @@
                    self.self_attn(
                        x,
                        mask,
                        mask_shift_chunk=mask_shift_chunk,
                        mask_shfit_chunk=mask_shfit_chunk,
                        mask_att_chunk_encoder=mask_att_chunk_encoder,
                    )
                )
@@ -118,7 +118,7 @@
                    self.self_attn(
                        x,
                        mask,
                        mask_shift_chunk=mask_shift_chunk,
                        mask_shfit_chunk=mask_shfit_chunk,
                        mask_att_chunk_encoder=mask_att_chunk_encoder,
                    )
                )
@@ -132,7 +132,7 @@
        if not self.normalize_before:
            x = self.norm2(x)

        return x, mask, cache, mask_shift_chunk, mask_att_chunk_encoder
        return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder

    def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0):
        """Compute encoded features.
@@ -198,7 +198,7 @@
        interctc_layer_idx: List[int] = [],
        interctc_use_conditioning: bool = False,
        kernel_size: int = 11,
        sanm_shift: int = 0,
        sanm_shfit: int = 0,
        selfattention_layer_type: str = "sanm",
    ):
        super().__init__()
@@ -277,7 +277,7 @@
                output_size,
                attention_dropout_rate,
                kernel_size,
                sanm_shift,
                sanm_shfit,
            )

            encoder_selfattn_layer_args = (
@@ -286,7 +286,7 @@
                output_size,
                attention_dropout_rate,
                kernel_size,
                sanm_shift,
                sanm_shfit,
            )

        self.encoders0 = repeat(

 funasr/models/ct_transformer_streaming/template.yaml

@@ -41,10 +41,10 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 5
    sanm_shfit: 5
    selfattention_layer_type: sanm
    padding_idx: 0

tokenizer: CharTokenizer
tokenizer_conf:
  unk_symbol: <unk>
  unk_symbol: <unk>

 funasr/models/monotonic_aligner/template.yaml

@@ -25,7 +25,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm

predictor: CifPredictorV3
@@ -111,5 +111,5 @@
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: true

    
normalize: null

 funasr/models/paraformer/decoder.py

@@ -248,7 +248,7 @@
        concat_after: bool = False,
        att_layer_num: int = 6,
        kernel_size: int = 21,
        sanm_shift: int = 0,
        sanm_shfit: int = 0,
        lora_list: List[str] = None,
        lora_rank: int = 8,
        lora_alpha: int = 16,
@@ -298,14 +298,14 @@

        self.att_layer_num = att_layer_num
        self.num_blocks = num_blocks
        if sanm_shift is None:
            sanm_shift = (kernel_size - 1) // 2
        if sanm_shfit is None:
            sanm_shfit = (kernel_size - 1) // 2
        self.decoders = repeat(
            att_layer_num,
            lambda lnum: DecoderLayerSANM(
                attention_dim,
                MultiHeadedAttentionSANMDecoder(
                    attention_dim, self_attention_dropout_rate, kernel_size, sanm_shift=sanm_shift
                    attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
                ),
                MultiHeadedAttentionCrossAtt(
                    attention_heads,
@@ -330,7 +330,7 @@
                lambda lnum: DecoderLayerSANM(
                    attention_dim,
                    MultiHeadedAttentionSANMDecoder(
                        attention_dim, self_attention_dropout_rate, kernel_size, sanm_shift=0
                        attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=0
                    ),
                    None,
                    PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
@@ -785,20 +785,20 @@
            for _ in range(cache_num)
        ]
        return (tgt, memory, pre_acoustic_embeds, cache)

    
    def is_optimizable(self):
        return True

    
    def get_input_names(self):
        cache_num = len(self.model.decoders) + len(self.model.decoders2)
        return ['tgt', 'memory', 'pre_acoustic_embeds'] \
               + ['cache_%d' % i for i in range(cache_num)]

    
    def get_output_names(self):
        cache_num = len(self.model.decoders) + len(self.model.decoders2)
        return ['y'] \
               + ['out_cache_%d' % i for i in range(cache_num)]

    
    def get_dynamic_axes(self):
        ret = {
            'tgt': {

 funasr/models/paraformer/template.yaml

@@ -29,7 +29,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm

# decoder
@@ -44,7 +44,7 @@
    src_attention_dropout_rate: 0.1
    att_layer_num: 16
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0

predictor: CifPredictorV2
predictor_conf:

 funasr/models/paraformer_streaming/model.py

@@ -198,10 +198,10 @@
            mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(
                None, device=encoder_out.device, batch_size=encoder_out.size(0)
            )
            mask_shift_chunk = self.encoder.overlap_chunk_cls.get_mask_shift_chunk(
            mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(
                None, device=encoder_out.device, batch_size=encoder_out.size(0)
            )
            encoder_out = encoder_out * mask_shift_chunk
            encoder_out = encoder_out * mask_shfit_chunk
        pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(
            encoder_out,
            ys_pad,
@@ -357,10 +357,10 @@
            mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(
                None, device=encoder_out.device, batch_size=encoder_out.size(0)
            )
            mask_shift_chunk = self.encoder.overlap_chunk_cls.get_mask_shift_chunk(
            mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(
                None, device=encoder_out.device, batch_size=encoder_out.size(0)
            )
            encoder_out = encoder_out * mask_shift_chunk
            encoder_out = encoder_out * mask_shfit_chunk
        pre_acoustic_embeds, pre_token_length, pre_alphas, pre_peak_index = self.predictor(
            encoder_out,
            None,

 funasr/models/paraformer_streaming/template.yaml

@@ -29,7 +29,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm
    chunk_size:
    - 12
@@ -59,7 +59,7 @@
    src_attention_dropout_rate: 0.1
    att_layer_num: 16
    kernel_size: 11
    sanm_shift: 5
    sanm_shfit: 5

predictor: CifPredictorV2
predictor_conf:

 funasr/models/sanm/attention.py

@@ -154,7 +154,7 @@
        n_feat,
        dropout_rate,
        kernel_size,
        sanm_shift=0,
        sanm_shfit=0,
        lora_list=None,
        lora_rank=8,
        lora_alpha=16,
@@ -199,17 +199,17 @@
        )
        # padding
        left_padding = (kernel_size - 1) // 2
        if sanm_shift > 0:
            left_padding = left_padding + sanm_shift
        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)

    def forward_fsmn(self, inputs, mask, mask_shift_chunk=None):
    def forward_fsmn(self, inputs, mask, mask_shfit_chunk=None):
        b, t, d = inputs.size()
        if mask is not None:
            mask = torch.reshape(mask, (b, -1, 1))
            if mask_shift_chunk is not None:
                mask = mask * mask_shift_chunk
            if mask_shfit_chunk is not None:
                mask = mask * mask_shfit_chunk
            inputs = inputs * mask

        x = inputs.transpose(1, 2)
@@ -289,7 +289,7 @@

        return self.linear_out(x)  # (batch, time1, d_model)

    def forward(self, x, mask, mask_shift_chunk=None, mask_att_chunk_encoder=None):
    def forward(self, x, mask, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
        """Compute scaled dot product attention.

        Args:
@@ -304,7 +304,7 @@

        """
        q_h, k_h, v_h, v = self.forward_qkv(x)
        fsmn_memory = self.forward_fsmn(v, mask, mask_shift_chunk)
        fsmn_memory = self.forward_fsmn(v, mask, mask_shfit_chunk)
        q_h = q_h * self.d_k ** (-0.5)
        scores = torch.matmul(q_h, k_h.transpose(-2, -1))
        att_outs = self.forward_attention(v_h, scores, mask, mask_att_chunk_encoder)
@@ -478,7 +478,7 @@

    """

    def __init__(self, n_feat, dropout_rate, kernel_size, sanm_shift=0):
    def __init__(self, n_feat, dropout_rate, kernel_size, sanm_shfit=0):
        """Construct an MultiHeadedAttention object."""
        super().__init__()

@@ -490,13 +490,13 @@
        # padding
        # padding
        left_padding = (kernel_size - 1) // 2
        if sanm_shift > 0:
            left_padding = left_padding + sanm_shift
        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_shift_chunk=None):
    def forward(self, inputs, mask, cache=None, mask_shfit_chunk=None):
        """
        :param x: (#batch, time1, size).
        :param mask: Mask tensor (#batch, 1, time)
@@ -509,9 +509,9 @@
        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_shift_chunk is not None:
                # logging.info("in fsmn, mask_fsmn: {}, {}".format(mask_shift_chunk.size(), mask_shift_chunk[0:100:50, :, :]))
                mask = mask * mask_shift_chunk
            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())

 funasr/models/sanm/decoder.py

@@ -226,7 +226,7 @@
        concat_after: bool = False,
        att_layer_num: int = 6,
        kernel_size: int = 21,
        sanm_shift: int = None,
        sanm_shfit: int = None,
        concat_embeds: bool = False,
        attention_dim: int = None,
        tf2torch_tensor_name_prefix_torch: str = "decoder",
@@ -271,14 +271,14 @@

        self.att_layer_num = att_layer_num
        self.num_blocks = num_blocks
        if sanm_shift is None:
            sanm_shift = (kernel_size - 1) // 2
        if sanm_shfit is None:
            sanm_shfit = (kernel_size - 1) // 2
        self.decoders = repeat(
            att_layer_num,
            lambda lnum: DecoderLayerSANM(
                attention_dim,
                MultiHeadedAttentionSANMDecoder(
                    attention_dim, self_attention_dropout_rate, kernel_size, sanm_shift=sanm_shift
                    attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
                ),
                MultiHeadedAttentionCrossAtt(
                    attention_heads,
@@ -303,7 +303,7 @@
                        attention_dim,
                        self_attention_dropout_rate,
                        kernel_size,
                        sanm_shift=sanm_shift,
                        sanm_shfit=sanm_shfit,
                    ),
                    None,
                    PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),

 funasr/models/sanm/encoder.py

@@ -69,7 +69,7 @@
        self.stochastic_depth_rate = stochastic_depth_rate
        self.dropout_rate = dropout_rate

    def forward(self, x, mask, cache=None, mask_shift_chunk=None, mask_att_chunk_encoder=None):
    def forward(self, x, mask, cache=None, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
        """Compute encoded features.

        Args:
@@ -106,7 +106,7 @@
                    self.self_attn(
                        x,
                        mask,
                        mask_shift_chunk=mask_shift_chunk,
                        mask_shfit_chunk=mask_shfit_chunk,
                        mask_att_chunk_encoder=mask_att_chunk_encoder,
                    ),
                ),
@@ -122,7 +122,7 @@
                    self.self_attn(
                        x,
                        mask,
                        mask_shift_chunk=mask_shift_chunk,
                        mask_shfit_chunk=mask_shfit_chunk,
                        mask_att_chunk_encoder=mask_att_chunk_encoder,
                    )
                )
@@ -131,7 +131,7 @@
                    self.self_attn(
                        x,
                        mask,
                        mask_shift_chunk=mask_shift_chunk,
                        mask_shfit_chunk=mask_shfit_chunk,
                        mask_att_chunk_encoder=mask_att_chunk_encoder,
                    )
                )
@@ -145,7 +145,7 @@
        if not self.normalize_before:
            x = self.norm2(x)

        return x, mask, cache, mask_shift_chunk, mask_att_chunk_encoder
        return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder

    def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0):
        """Compute encoded features.
@@ -212,7 +212,7 @@
        interctc_layer_idx: List[int] = [],
        interctc_use_conditioning: bool = False,
        kernel_size: int = 11,
        sanm_shift: int = 0,
        sanm_shfit: int = 0,
        lora_list: List[str] = None,
        lora_rank: int = 8,
        lora_alpha: int = 16,
@@ -299,7 +299,7 @@
                output_size,
                attention_dropout_rate,
                kernel_size,
                sanm_shift,
                sanm_shfit,
                lora_list,
                lora_rank,
                lora_alpha,
@@ -312,7 +312,7 @@
                output_size,
                attention_dropout_rate,
                kernel_size,
                sanm_shift,
                sanm_shfit,
                lora_list,
                lora_rank,
                lora_alpha,

 funasr/models/sanm/template.yaml

@@ -26,7 +26,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm

# decoder
@@ -41,7 +41,7 @@
    src_attention_dropout_rate: 0.1
    att_layer_num: 16
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0




 funasr/models/scama/chunk_utilis.py

@@ -21,7 +21,7 @@
        stride: tuple = (10,),
        pad_left: tuple = (0,),
        encoder_att_look_back_factor: tuple = (1,),
        shift_fsmn: int = 0,
        shfit_fsmn: int = 0,
        decoder_att_look_back_factor: tuple = (1,),
    ):

@@ -45,11 +45,11 @@
            encoder_att_look_back_factor,
            decoder_att_look_back_factor,
        )
        self.shift_fsmn = shift_fsmn
        self.shfit_fsmn = shfit_fsmn
        self.x_add_mask = None
        self.x_rm_mask = None
        self.x_len = None
        self.mask_shift_chunk = None
        self.mask_shfit_chunk = None
        self.mask_chunk_predictor = None
        self.mask_att_chunk_encoder = None
        self.mask_shift_att_chunk_decoder = None
@@ -88,7 +88,7 @@
            stride,
            pad_left,
            encoder_att_look_back_factor,
            chunk_size + self.shift_fsmn,
            chunk_size + self.shfit_fsmn,
            decoder_att_look_back_factor,
        )
        return (
@@ -118,13 +118,13 @@
            chunk_size, stride, pad_left, encoder_att_look_back_factor, chunk_size_pad_shift = (
                self.get_chunk_size(ind)
            )
            shift_fsmn = self.shift_fsmn
            shfit_fsmn = self.shfit_fsmn
            pad_right = chunk_size - stride - pad_left

            chunk_num_batch = np.ceil(x_len / stride).astype(np.int32)
            x_len_chunk = (
                (chunk_num_batch - 1) * chunk_size_pad_shift
                + shift_fsmn
                + shfit_fsmn
                + pad_left
                + 0
                + x_len
@@ -138,13 +138,13 @@
            max_len_for_x_mask_tmp = max(chunk_size, x_len_max + pad_left)
            x_add_mask = np.zeros([0, max_len_for_x_mask_tmp], dtype=dtype)
            x_rm_mask = np.zeros([max_len_for_x_mask_tmp, 0], dtype=dtype)
            mask_shift_chunk = np.zeros([0, num_units], dtype=dtype)
            mask_shfit_chunk = np.zeros([0, num_units], dtype=dtype)
            mask_chunk_predictor = np.zeros([0, num_units_predictor], dtype=dtype)
            mask_shift_att_chunk_decoder = np.zeros([0, 1], dtype=dtype)
            mask_att_chunk_encoder = np.zeros([0, chunk_num * chunk_size_pad_shift], dtype=dtype)
            for chunk_ids in range(chunk_num):
                # x_mask add
                fsmn_padding = np.zeros((shift_fsmn, max_len_for_x_mask_tmp), dtype=dtype)
                fsmn_padding = np.zeros((shfit_fsmn, max_len_for_x_mask_tmp), dtype=dtype)
                x_mask_cur = np.diag(np.ones(chunk_size, dtype=np.float32))
                x_mask_pad_left = np.zeros((chunk_size, chunk_ids * stride), dtype=dtype)
                x_mask_pad_right = np.zeros((chunk_size, max_len_for_x_mask_tmp), dtype=dtype)
@@ -154,7 +154,7 @@
                x_add_mask = np.concatenate([x_add_mask, x_add_mask_fsmn], axis=0)

                # x_mask rm
                fsmn_padding = np.zeros((max_len_for_x_mask_tmp, shift_fsmn), dtype=dtype)
                fsmn_padding = np.zeros((max_len_for_x_mask_tmp, shfit_fsmn), dtype=dtype)
                padding_mask_left = np.zeros((max_len_for_x_mask_tmp, pad_left), dtype=dtype)
                padding_mask_right = np.zeros((max_len_for_x_mask_tmp, pad_right), dtype=dtype)
                x_mask_cur = np.diag(np.ones(stride, dtype=dtype))
@@ -170,13 +170,13 @@
                x_rm_mask = np.concatenate([x_rm_mask, x_rm_mask_cur_fsmn], axis=1)

                # fsmn_padding_mask
                pad_shift_mask = np.zeros([shift_fsmn, num_units], dtype=dtype)
                pad_shfit_mask = np.zeros([shfit_fsmn, num_units], dtype=dtype)
                ones_1 = np.ones([chunk_size, num_units], dtype=dtype)
                mask_shift_chunk_cur = np.concatenate([pad_shift_mask, ones_1], axis=0)
                mask_shift_chunk = np.concatenate([mask_shift_chunk, mask_shift_chunk_cur], axis=0)
                mask_shfit_chunk_cur = np.concatenate([pad_shfit_mask, ones_1], axis=0)
                mask_shfit_chunk = np.concatenate([mask_shfit_chunk, mask_shfit_chunk_cur], axis=0)

                # predictor mask
                zeros_1 = np.zeros([shift_fsmn + pad_left, num_units_predictor], dtype=dtype)
                zeros_1 = np.zeros([shfit_fsmn + pad_left, num_units_predictor], dtype=dtype)
                ones_2 = np.ones([stride, num_units_predictor], dtype=dtype)
                zeros_3 = np.zeros(
                    [chunk_size - stride - pad_left, num_units_predictor], dtype=dtype
@@ -188,13 +188,13 @@
                )

                # encoder att mask
                zeros_1_top = np.zeros([shift_fsmn, chunk_num * chunk_size_pad_shift], dtype=dtype)
                zeros_1_top = np.zeros([shfit_fsmn, chunk_num * chunk_size_pad_shift], dtype=dtype)

                zeros_2_num = max(chunk_ids - encoder_att_look_back_factor, 0)
                zeros_2 = np.zeros([chunk_size, zeros_2_num * chunk_size_pad_shift], dtype=dtype)

                encoder_att_look_back_num = max(chunk_ids - zeros_2_num, 0)
                zeros_2_left = np.zeros([chunk_size, shift_fsmn], dtype=dtype)
                zeros_2_left = np.zeros([chunk_size, shfit_fsmn], dtype=dtype)
                ones_2_mid = np.ones([stride, stride], dtype=dtype)
                zeros_2_bottom = np.zeros([chunk_size - stride, stride], dtype=dtype)
                zeros_2_right = np.zeros([chunk_size, chunk_size - stride], dtype=dtype)
@@ -202,7 +202,7 @@
                ones_2 = np.concatenate([zeros_2_left, ones_2, zeros_2_right], axis=1)
                ones_2 = np.tile(ones_2, [1, encoder_att_look_back_num])

                zeros_3_left = np.zeros([chunk_size, shift_fsmn], dtype=dtype)
                zeros_3_left = np.zeros([chunk_size, shfit_fsmn], dtype=dtype)
                ones_3_right = np.ones([chunk_size, chunk_size], dtype=dtype)
                ones_3 = np.concatenate([zeros_3_left, ones_3_right], axis=1)

@@ -218,7 +218,7 @@
                )

                # decoder fsmn_shift_att_mask
                zeros_1 = np.zeros([shift_fsmn, 1])
                zeros_1 = np.zeros([shfit_fsmn, 1])
                ones_1 = np.ones([chunk_size, 1])
                mask_shift_att_chunk_decoder_cur = np.concatenate([zeros_1, ones_1], axis=0)
                mask_shift_att_chunk_decoder = np.concatenate(
@@ -229,7 +229,7 @@
            self.x_len_chunk = x_len_chunk
            self.x_rm_mask = x_rm_mask[:x_len_max, :x_len_chunk_max]
            self.x_len = x_len
            self.mask_shift_chunk = mask_shift_chunk[:x_len_chunk_max, :]
            self.mask_shfit_chunk = mask_shfit_chunk[:x_len_chunk_max, :]
            self.mask_chunk_predictor = mask_chunk_predictor[:x_len_chunk_max, :]
            self.mask_att_chunk_encoder = mask_att_chunk_encoder[:x_len_chunk_max, :x_len_chunk_max]
            self.mask_shift_att_chunk_decoder = mask_shift_att_chunk_decoder[:x_len_chunk_max, :]
@@ -238,7 +238,7 @@
                self.x_len_chunk,
                self.x_rm_mask,
                self.x_len,
                self.mask_shift_chunk,
                self.mask_shfit_chunk,
                self.mask_chunk_predictor,
                self.mask_att_chunk_encoder,
                self.mask_shift_att_chunk_decoder,
@@ -309,7 +309,7 @@
            x = torch.from_numpy(x).type(dtype).to(device)
        return x

    def get_mask_shift_chunk(
    def get_mask_shfit_chunk(
        self, chunk_outs=None, device="cpu", batch_size=1, num_units=1, idx=4, dtype=torch.float32
    ):
        with torch.no_grad():

 funasr/models/scama/decoder.py

@@ -226,7 +226,7 @@
        concat_after: bool = False,
        att_layer_num: int = 6,
        kernel_size: int = 21,
        sanm_shift: int = None,
        sanm_shfit: int = None,
        concat_embeds: bool = False,
        attention_dim: int = None,
        tf2torch_tensor_name_prefix_torch: str = "decoder",
@@ -271,14 +271,14 @@

        self.att_layer_num = att_layer_num
        self.num_blocks = num_blocks
        if sanm_shift is None:
            sanm_shift = (kernel_size - 1) // 2
        if sanm_shfit is None:
            sanm_shfit = (kernel_size - 1) // 2
        self.decoders = repeat(
            att_layer_num,
            lambda lnum: DecoderLayerSANM(
                attention_dim,
                MultiHeadedAttentionSANMDecoder(
                    attention_dim, self_attention_dropout_rate, kernel_size, sanm_shift=sanm_shift
                    attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
                ),
                MultiHeadedAttentionCrossAtt(
                    attention_heads,
@@ -303,7 +303,7 @@
                        attention_dim,
                        self_attention_dropout_rate,
                        kernel_size,
                        sanm_shift=sanm_shift,
                        sanm_shfit=sanm_shfit,
                    ),
                    None,
                    PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),

 funasr/models/scama/encoder.py

@@ -69,7 +69,7 @@
        self.stochastic_depth_rate = stochastic_depth_rate
        self.dropout_rate = dropout_rate

    def forward(self, x, mask, cache=None, mask_shift_chunk=None, mask_att_chunk_encoder=None):
    def forward(self, x, mask, cache=None, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
        """Compute encoded features.

        Args:
@@ -106,7 +106,7 @@
                    self.self_attn(
                        x,
                        mask,
                        mask_shift_chunk=mask_shift_chunk,
                        mask_shfit_chunk=mask_shfit_chunk,
                        mask_att_chunk_encoder=mask_att_chunk_encoder,
                    ),
                ),
@@ -122,7 +122,7 @@
                    self.self_attn(
                        x,
                        mask,
                        mask_shift_chunk=mask_shift_chunk,
                        mask_shfit_chunk=mask_shfit_chunk,
                        mask_att_chunk_encoder=mask_att_chunk_encoder,
                    )
                )
@@ -131,7 +131,7 @@
                    self.self_attn(
                        x,
                        mask,
                        mask_shift_chunk=mask_shift_chunk,
                        mask_shfit_chunk=mask_shfit_chunk,
                        mask_att_chunk_encoder=mask_att_chunk_encoder,
                    )
                )
@@ -145,7 +145,7 @@
        if not self.normalize_before:
            x = self.norm2(x)

        return x, mask, cache, mask_shift_chunk, mask_att_chunk_encoder
        return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder

    def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0):
        """Compute encoded features.
@@ -212,7 +212,7 @@
        interctc_layer_idx: List[int] = [],
        interctc_use_conditioning: bool = False,
        kernel_size: int = 11,
        sanm_shift: int = 0,
        sanm_shfit: int = 0,
        selfattention_layer_type: str = "sanm",
        chunk_size: Union[int, Sequence[int]] = (16,),
        stride: Union[int, Sequence[int]] = (10,),
@@ -299,7 +299,7 @@
                output_size,
                attention_dropout_rate,
                kernel_size,
                sanm_shift,
                sanm_shfit,
            )

            encoder_selfattn_layer_args = (
@@ -308,7 +308,7 @@
                output_size,
                attention_dropout_rate,
                kernel_size,
                sanm_shift,
                sanm_shfit,
            )
        self.encoders0 = repeat(
            1,
@@ -343,12 +343,12 @@
            assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
        self.interctc_use_conditioning = interctc_use_conditioning
        self.conditioning_layer = None
        shift_fsmn = (kernel_size - 1) // 2
        shfit_fsmn = (kernel_size - 1) // 2
        self.overlap_chunk_cls = overlap_chunk(
            chunk_size=chunk_size,
            stride=stride,
            pad_left=pad_left,
            shift_fsmn=shift_fsmn,
            shfit_fsmn=shfit_fsmn,
            encoder_att_look_back_factor=encoder_att_look_back_factor,
            decoder_att_look_back_factor=decoder_att_look_back_factor,
        )
@@ -397,31 +397,31 @@
        else:
            xs_pad = self.embed(xs_pad)

        mask_shift_chunk, mask_att_chunk_encoder = None, None
        mask_shfit_chunk, mask_att_chunk_encoder = None, None
        if self.overlap_chunk_cls is not None:
            ilens = masks.squeeze(1).sum(1)
            chunk_outs = self.overlap_chunk_cls.gen_chunk_mask(ilens, ind)
            xs_pad, ilens = self.overlap_chunk_cls.split_chunk(xs_pad, ilens, chunk_outs=chunk_outs)
            masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
            mask_shift_chunk = self.overlap_chunk_cls.get_mask_shift_chunk(
            mask_shfit_chunk = self.overlap_chunk_cls.get_mask_shfit_chunk(
                chunk_outs, xs_pad.device, xs_pad.size(0), dtype=xs_pad.dtype
            )
            mask_att_chunk_encoder = self.overlap_chunk_cls.get_mask_att_chunk_encoder(
                chunk_outs, xs_pad.device, xs_pad.size(0), dtype=xs_pad.dtype
            )

        encoder_outs = self.encoders0(xs_pad, masks, None, mask_shift_chunk, mask_att_chunk_encoder)
        encoder_outs = self.encoders0(xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder)
        xs_pad, masks = encoder_outs[0], encoder_outs[1]
        intermediate_outs = []
        if len(self.interctc_layer_idx) == 0:
            encoder_outs = self.encoders(
                xs_pad, masks, None, mask_shift_chunk, mask_att_chunk_encoder
                xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder
            )
            xs_pad, masks = encoder_outs[0], encoder_outs[1]
        else:
            for layer_idx, encoder_layer in enumerate(self.encoders):
                encoder_outs = encoder_layer(
                    xs_pad, masks, None, mask_shift_chunk, mask_att_chunk_encoder
                    xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder
                )
                xs_pad, masks = encoder_outs[0], encoder_outs[1]
                if layer_idx + 1 in self.interctc_layer_idx:

 funasr/models/scama/model.py

@@ -321,10 +321,10 @@
            mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(
                None, device=encoder_out.device, batch_size=encoder_out.size(0)
            )
            mask_shift_chunk = self.encoder.overlap_chunk_cls.get_mask_shift_chunk(
            mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(
                None, device=encoder_out.device, batch_size=encoder_out.size(0)
            )
            encoder_out = encoder_out * mask_shift_chunk
            encoder_out = encoder_out * mask_shfit_chunk
        pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(
            encoder_out,
            ys_out_pad,
@@ -415,10 +415,10 @@
        mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(
            None, device=encoder_out.device, batch_size=encoder_out.size(0)
        )
        mask_shift_chunk = self.encoder.overlap_chunk_cls.get_mask_shift_chunk(
        mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(
            None, device=encoder_out.device, batch_size=encoder_out.size(0)
        )
        encoder_out = encoder_out * mask_shift_chunk
        encoder_out = encoder_out * mask_shfit_chunk
        pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(
            encoder_out,
            ys_out_pad,

 funasr/models/scama/template.yaml

@@ -26,7 +26,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm

# decoder
@@ -41,7 +41,7 @@
    src_attention_dropout_rate: 0.1
    att_layer_num: 16
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0

predictor: CifPredictorV2
predictor_conf:

 funasr/models/seaco_paraformer/template.yaml

@@ -36,7 +36,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm

# decoder
@@ -51,7 +51,7 @@
    src_attention_dropout_rate: 0.1
    att_layer_num: 16
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0

# seaco decoder
seaco_decoder: ParaformerSANMDecoder
@@ -64,7 +64,7 @@
    self_attention_dropout_rate: 0.1
    src_attention_dropout_rate: 0.1
    kernel_size: 21
    sanm_shift: 0
    sanm_shfit: 0
    use_output_layer: false
    wo_input_layer: true


 funasr/models/sense_voice/model.py

@@ -95,7 +95,7 @@
        n_feat,
        dropout_rate,
        kernel_size,
        sanm_shift=0,
        sanm_shfit=0,
        lora_list=None,
        lora_rank=8,
        lora_alpha=16,
@@ -121,17 +121,17 @@
        )
        # padding
        left_padding = (kernel_size - 1) // 2
        if sanm_shift > 0:
            left_padding = left_padding + sanm_shift
        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)

    def forward_fsmn(self, inputs, mask, mask_shift_chunk=None):
    def forward_fsmn(self, inputs, mask, mask_shfit_chunk=None):
        b, t, d = inputs.size()
        if mask is not None:
            mask = torch.reshape(mask, (b, -1, 1))
            if mask_shift_chunk is not None:
                mask = mask * mask_shift_chunk
            if mask_shfit_chunk is not None:
                mask = mask * mask_shfit_chunk
            inputs = inputs * mask

        x = inputs.transpose(1, 2)
@@ -211,7 +211,7 @@

        return self.linear_out(x)  # (batch, time1, d_model)

    def forward(self, x, mask, mask_shift_chunk=None, mask_att_chunk_encoder=None):
    def forward(self, x, mask, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
        """Compute scaled dot product attention.

        Args:
@@ -226,7 +226,7 @@

        """
        q_h, k_h, v_h, v = self.forward_qkv(x)
        fsmn_memory = self.forward_fsmn(v, mask, mask_shift_chunk)
        fsmn_memory = self.forward_fsmn(v, mask, mask_shfit_chunk)
        q_h = q_h * self.d_k ** (-0.5)
        scores = torch.matmul(q_h, k_h.transpose(-2, -1))
        att_outs = self.forward_attention(v_h, scores, mask, mask_att_chunk_encoder)
@@ -326,7 +326,7 @@
        self.stochastic_depth_rate = stochastic_depth_rate
        self.dropout_rate = dropout_rate

    def forward(self, x, mask, cache=None, mask_shift_chunk=None, mask_att_chunk_encoder=None):
    def forward(self, x, mask, cache=None, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
        """Compute encoded features.

        Args:
@@ -363,7 +363,7 @@
                    self.self_attn(
                        x,
                        mask,
                        mask_shift_chunk=mask_shift_chunk,
                        mask_shfit_chunk=mask_shfit_chunk,
                        mask_att_chunk_encoder=mask_att_chunk_encoder,
                    ),
                ),
@@ -379,7 +379,7 @@
                    self.self_attn(
                        x,
                        mask,
                        mask_shift_chunk=mask_shift_chunk,
                        mask_shfit_chunk=mask_shfit_chunk,
                        mask_att_chunk_encoder=mask_att_chunk_encoder,
                    )
                )
@@ -388,7 +388,7 @@
                    self.self_attn(
                        x,
                        mask,
                        mask_shift_chunk=mask_shift_chunk,
                        mask_shfit_chunk=mask_shfit_chunk,
                        mask_att_chunk_encoder=mask_att_chunk_encoder,
                    )
                )
@@ -402,7 +402,7 @@
        if not self.normalize_before:
            x = self.norm2(x)

        return x, mask, cache, mask_shift_chunk, mask_att_chunk_encoder
        return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder

    def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0):
        """Compute encoded features.
@@ -469,7 +469,7 @@
        positionwise_conv_kernel_size: int = 1,
        padding_idx: int = -1,
        kernel_size: int = 11,
        sanm_shift: int = 0,
        sanm_shfit: int = 0,
        selfattention_layer_type: str = "sanm",
        **kwargs,
    ):
@@ -494,7 +494,7 @@
            output_size,
            attention_dropout_rate,
            kernel_size,
            sanm_shift,
            sanm_shfit,
        )
        encoder_selfattn_layer_args = (
            attention_heads,
@@ -502,7 +502,7 @@
            output_size,
            attention_dropout_rate,
            kernel_size,
            sanm_shift,
            sanm_shfit,
        )

        self.encoders0 = nn.ModuleList(

 funasr/models/sond/encoder/fsmn_encoder.py

@@ -36,12 +36,12 @@
        right_padding = kernel_size - 1 - left_padding
        self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0)

    def forward(self, inputs, mask, mask_shift_chunk=None):
    def forward(self, inputs, mask, mask_shfit_chunk=None):
        b, t, d = inputs.size()
        if mask is not None:
            mask = torch.reshape(mask, (b, -1, 1))
            if mask_shift_chunk is not None:
                mask = mask * mask_shift_chunk
            if mask_shfit_chunk is not None:
                mask = mask * mask_shfit_chunk

        inputs = inputs * mask
        x = inputs.transpose(1, 2)

 funasr/models/uniasr/model.py

@@ -521,10 +521,10 @@
            mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(
                None, device=encoder_out.device, batch_size=encoder_out.size(0)
            )
            mask_shift_chunk = self.encoder.overlap_chunk_cls.get_mask_shift_chunk(
            mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(
                None, device=encoder_out.device, batch_size=encoder_out.size(0)
            )
            encoder_out = encoder_out * mask_shift_chunk
            encoder_out = encoder_out * mask_shfit_chunk
        pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(
            encoder_out,
            ys_out_pad,
@@ -622,10 +622,10 @@
            mask_chunk_predictor = self.encoder2.overlap_chunk_cls.get_mask_chunk_predictor(
                None, device=encoder_out.device, batch_size=encoder_out.size(0)
            )
            mask_shift_chunk = self.encoder2.overlap_chunk_cls.get_mask_shift_chunk(
            mask_shfit_chunk = self.encoder2.overlap_chunk_cls.get_mask_shfit_chunk(
                None, device=encoder_out.device, batch_size=encoder_out.size(0)
            )
            encoder_out = encoder_out * mask_shift_chunk
            encoder_out = encoder_out * mask_shfit_chunk
        pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor2(
            encoder_out,
            ys_out_pad,
@@ -724,10 +724,10 @@
            mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(
                None, device=encoder_out.device, batch_size=encoder_out.size(0)
            )
            mask_shift_chunk = self.encoder.overlap_chunk_cls.get_mask_shift_chunk(
            mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(
                None, device=encoder_out.device, batch_size=encoder_out.size(0)
            )
            encoder_out = encoder_out * mask_shift_chunk
            encoder_out = encoder_out * mask_shfit_chunk
        pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(
            encoder_out,
            ys_out_pad,
@@ -806,10 +806,10 @@
            mask_chunk_predictor = self.encoder2.overlap_chunk_cls.get_mask_chunk_predictor(
                None, device=encoder_out.device, batch_size=encoder_out.size(0)
            )
            mask_shift_chunk = self.encoder2.overlap_chunk_cls.get_mask_shift_chunk(
            mask_shfit_chunk = self.encoder2.overlap_chunk_cls.get_mask_shfit_chunk(
                None, device=encoder_out.device, batch_size=encoder_out.size(0)
            )
            encoder_out = encoder_out * mask_shift_chunk
            encoder_out = encoder_out * mask_shfit_chunk
        pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor2(
            encoder_out,
            ys_out_pad,

 funasr/models/uniasr/template.yaml

@@ -33,7 +33,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm
    chunk_size:
    - 20
@@ -89,7 +89,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 21
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm
    chunk_size:
    - 45

 runtime/csharp/AliParaformerAsr/AliParaformerAsr/Model/DecoderConfEntity.cs

@@ -19,7 +19,7 @@
        private float _src_attention_dropout_rate = 0.1F;
        private int _att_layer_num = 16;
        private int _kernel_size = 11;
        private int _sanm_shift = 0;
        private int _sanm_shfit = 0;

        public int attention_heads { get => _attention_heads; set => _attention_heads = value; }
        public int linear_units { get => _linear_units; set => _linear_units = value; }
@@ -30,7 +30,7 @@
        public float src_attention_dropout_rate { get => _src_attention_dropout_rate; set => _src_attention_dropout_rate = value; }
        public int att_layer_num { get => _att_layer_num; set => _att_layer_num = value; }
        public int kernel_size { get => _kernel_size; set => _kernel_size = value; }
        public int sanm_shift { get => _sanm_shift; set => _sanm_shift = value; }

        public int sanm_shfit { get => _sanm_shfit; set => _sanm_shfit = value; }
        
    }
}

 runtime/csharp/AliParaformerAsr/AliParaformerAsr/Model/EncoderConfEntity.cs

@@ -21,7 +21,7 @@
        private string _pos_enc_class = "SinusoidalPositionEncoder";
        private bool _normalize_before = true;
        private int _kernel_size = 11;
        private int _sanm_shift = 0;
        private int _sanm_shfit = 0;
        private string _selfattention_layer_type = "sanm";

        public int output_size { get => _output_size; set => _output_size = value; }
@@ -35,7 +35,7 @@
        public string pos_enc_class { get => _pos_enc_class; set => _pos_enc_class = value; }
        public bool normalize_before { get => _normalize_before; set => _normalize_before = value; }
        public int kernel_size { get => _kernel_size; set => _kernel_size = value; }
        public int sanm_shift { get => _sanm_shift; set => _sanm_shift = value; }
        public int sanm_shfit { get => _sanm_shfit; set => _sanm_shfit = value; }
        public string selfattention_layer_type { get => _selfattention_layer_type; set => _selfattention_layer_type = value; }
    }
}

 runtime/triton_gpu/model_repo_paraformer_large_online/feature_extractor/config.yaml

@@ -8593,7 +8593,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm
    chunk_size:
    - 12
@@ -8623,7 +8623,7 @@
    src_attention_dropout_rate: 0.1
    att_layer_num: 16
    kernel_size: 11
    sanm_shift: 5
    sanm_shfit: 5
predictor: cif_predictor_v2
predictor_conf:
    idim: 512

 runtime/triton_gpu/model_repo_sense_voice_small/feature_extractor/config.yaml

@@ -12,7 +12,7 @@
    pos_enc_class: SinusoidalPositionEncoder
    normalize_before: true
    kernel_size: 11
    sanm_shift: 0
    sanm_shfit: 0
    selfattention_layer_type: sanm