Check if all the given tokens are in token symbol table. Args: token_sym_table: Token symbol table that contains all the valid toke

NOTE(xcsong): The "units.txt" file is solely for adapting to the training API of Wenet and for quickly checking the corresponding tex

NOTE(xcsong): The "units.txt" file is solely for adapting to the training API of Wenet and for quickly checking the corresponding tex

Export interface for c++ call, apply linear transform and log softmax before ctc Args: xs (torch.Tensor): encoder out

Cross validation on

计算正确率和错误率，并列出混淆矩阵 :return:

10 batch , 1 worker : 15.9s, nvidia-smi 在8卡使用 10batch, 10worker: 16.02s, nvidia-smi 在8卡使用 10 batch ,10worker , 310tar: 17.46s， 10 bat

...

可视化混淆矩阵的函数 :param labels: 标签序列，类型为列表等可迭代对象 :param matrix: 代表混淆矩阵的二维列表，元素为整数，形状应为(len(labels), len(labels))

download from url to dest

Dynamic batch the data until the total frames in batch reach `max_frames_in_batch` Args: data: Iterable[{key, feat, labe

Dynamic batch the data until the total frames in batch reach `max_frames_in_batch` Args: data: Iterable[{key, feat, labe

Dynamic batch the data until the total frames in batch reach `max_frames_in_batch` Args: data: Iterable[{key, feat, labe

Dynamic batch the data until the total frames in batch reach `max_frames_in_batch` Args: data: Iterable[{key, feat, labe

Dynamic batch the data until the total frames in batch reach `max_frames_in_batch` Args: data: Iterable[{key, feat, labe

Dynamic batch the data until the total frames in batch reach `max_frames_in_batch` Args: data: Iterable[{key, feat, labe

Export interface for c++ call, return eos symbol id of the model

When reaching the end of the decoded sequence, we need to finalize the matching, the purpose is to subtract the added bonus score for the

When reaching the end of the decoded sequence, we need to finalize the matching, the purpose is to subtract the added bonus score for the

Forward diffusion Args: mu (torch.Tensor): output of encoder shape: (batch_size, n_feats, mel_timesteps)

Identity with 4-D dataflow, input == output. Args: x (torch.Tensor): (batch, in_channel, 1, time) Returns: (t

Linear with 4-D dataflow. Args: x (torch.Tensor): (batch, in_channel, 1, time) Returns: (torch.Tensor): (batch

Subsample x with 4-D dataflow. Args: x (torch.Tensor): Input tensor (#batch, 1, mel_dim, time). Returns: torc

Compute scaled dot product attention. Args: q (torch.Tensor): Query tensor (#batch, size, 1, chunk_size). k (torch.Te

Compute convolution module. Args: x (torch.Tensor): Input tensor (#batch, channels, 1, chunk_size). cache (torch.Tenso

Forward function. Args: xs: input tensor (B, D, 1, L) Returns: output tensor, (B, D, 1, L)

Compute encoded features. Args: x (torch.Tensor): (#batch, size, 1, chunk_size) att_mask (torch.Tensor): Mask tensor

Forward just one chunk Args: xs (torch.Tensor): chunk input, with shape (b=1, 1, time, mel-dim), where `time ==

frame activations, without softmax. Args: Tensor x: 4d tensor (B, hidden_dim, 1, chunk_size) Returns: torch.T

Compute attention context vector. Args: value (torch.Tensor): Transformed value, size (#batch, n_head, time2, d_k

Compute attention context vector. Args: value (torch.Tensor): Transformed value, size (#batch, n_head, time2, d_k

Export interface for c++ call, forward decoder with multiple hypothesis from ctc prefix beam search and one encoder output Args:

Export interface for c++ call, forward decoder with multiple hypothesis from ctc prefix beam search and one encoder output Args:

Forward just one chunk Args: xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim), where `time == (ch

Forward just one chunk Args: xs (torch.Tensor): chunk input offset (int): current offset in encoder output time stam

Chunk-based context mode Frontend + Encoder + Decoder + Calc loss Args: speech: (Batch, Length, ...) speech_l

Forward input chunk by chunk with chunk_size like a streaming fashion Here we should pay special attention to computation cache

Forward input chunk by chunk with chunk_size like a streaming fashion Here we should pay special attention to computation cache