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espnet2.gan_codec.espnet_model.ESPnetGANCodecModel

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espnet2.gan_codec.espnet_model.ESPnetGANCodecModel

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class espnet2.gan_codec.espnet_model.ESPnetGANCodecModel(codec: AbsGANCodec)

Bases: AbsGANESPnetModel

ESPnet model for GAN-based neural codec task.

This class implements a GAN-based neural codec model for audio processing tasks. It utilizes a generator and discriminator architecture to encode and decode audio waveforms.

codec

An instance of a codec which contains the generator and discriminator modules required for encoding and decoding audio.

  • Type:AbsGANCodec

  • Parameters:codec (AbsGANCodec) – An instance of a codec that must have ‘generator’ and ‘discriminator’ attributes.

  • Raises:AssertionError – If the provided codec does not have the required ‘generator’ or ‘discriminator’ attributes.

################### Examples

>>> from espnet2.gan_codec import MyGANCodec  # hypothetical import
>>> model = ESPnetGANCodecModel(codec=MyGANCodec())
>>> audio_tensor = torch.randn(1, 16000)  # Example audio tensor
>>> loss_info = model.forward(audio_tensor)
>>> encoded = model.encode(audio_tensor)
>>> decoded = model.decode(encoded)

############ NOTE This model is designed for tasks involving GAN-based audio codec processing. It supports encoding and decoding with options for continuous representations.

Initialize ESPnetGANCodecModel module.

collect_feats(audio: Tensor, **kwargs) → Dict[str, Tensor]

Calculate features and return them as a dictionary.

This method processes the input audio waveform tensor and extracts relevant features, returning them in a dictionary format. The dictionary keys correspond to different types of features that can be used for further processing or analysis.

  • Parameters:
    • audio (Tensor) – Audio waveform tensor of shape (B, T_wav), where B is the batch size and T_wav is the number of audio samples.
    • kwargs – Additional keyword arguments for future extensibility.
  • Returns: A dictionary containing the extracted features, : where each key is a string representing the feature name and the corresponding value is a tensor of the feature data.
  • Return type: Dict[str, Tensor]

################### Examples

>>> model = ESPnetGANCodecModel(codec)
>>> audio_input = torch.randn(2, 16000)  # Example audio tensor
>>> features = model.collect_feats(audio_input)
>>> print(features.keys())  # Output might include feature names

############ NOTE This method is currently a placeholder and returns an empty dictionary. Implement feature extraction logic as needed.

decode(codes: Tensor)

Codec Decoding Process.

This method decodes the provided codec tokens into an audio waveform.

  • Parameters:codes (Tensor) – codec tokens with shape [N_stream, B, T], where:
    • N_stream: Number of streams in the codec.
    • B: Batch size.
    • T: Length of the tokens.
  • Returns: Generated waveform with shape (B, 1, n_sample), where: : - B: Batch size.
    • 1: Single channel for the audio waveform.
    • n_sample: Number of samples in the generated waveform.
  • Return type: Tensor

################### Examples

>>> codec_model = ESPnetGANCodecModel(codec)
>>> codes = torch.randn(2, 4, 256)  # Example codec tokens
>>> waveform = codec_model.decode(codes)
>>> print(waveform.shape)  # Output shape: (4, 1, n_sample)

############ NOTE Ensure that the input codes tensor is in the correct format to avoid runtime errors.

decode_continuous(z: Tensor)

Codec Decoding Process without dequantization.

This method takes a continuous codec representation and decodes it into an audio waveform. The input tensor should represent the continuous features obtained from the encoding process, and the output is a reconstructed waveform tensor.

  • Parameters:z (Tensor) – Continuous codec representation with shape (B, D, T), where B is the batch size, D is the number of dimensions, and T is the length of the sequence.
  • Returns: Generated waveform with shape (B, 1, n_sample), where n_sample : is the number of samples in the reconstructed waveform.
  • Return type: Tensor

################### Examples

>>> model = ESPnetGANCodecModel(codec)
>>> continuous_codes = torch.randn(2, 256, 100)  # Example input
>>> waveform = model.decode_continuous(continuous_codes)
>>> print(waveform.shape)  # Output shape should be (2, 1, n_sample)

############ NOTE Ensure that the input tensor z is properly shaped as specified above to avoid runtime errors during the decoding process.

encode(audio: Tensor, **kwargs)

Codec Encoding Process.

This method encodes audio waveforms into codec representations. It handles different input tensor shapes, ensuring they are compatible with the encoding process. The resulting encoded output can be used for various applications in the GAN-based neural codec framework.

  • Parameters:audio (Tensor) – Audio waveform tensor, which can have the shape:
    • (B, 1, T_wav) for batched audio with a single channel.
    • (B, T_wav) for batched audio without explicit channel.
    • (T_wav) for a single audio sample.
  • Returns: Generated codecs in the shape (N_stream, B, T), where: : - N_stream: Number of codec streams.
    • B: Batch size.
    • T: Length of the encoded representation.
  • Return type: Tensor

################### Examples

>>> model = ESPnetGANCodecModel(codec)
>>> audio = torch.randn(2, 1, 16000)  # Example audio tensor
>>> encoded = model.encode(audio)
>>> print(encoded.shape)  # Output shape should be (N_stream, 2, T)

############ NOTE Ensure that the input audio tensor is properly shaped as described in the Args section for successful encoding.

encode_continuous(audio)

Codec Encoding Process without quantization.

This method encodes the given audio input into a continuous codec representation without applying any quantization. It ensures that the audio input is reshaped appropriately before passing it to the encoder.

  • Parameters:audio (Tensor) – Audio waveform tensor with shapes: (B, 1, T_wav), (B, T_wav), or (T_wav).
  • Returns: Generated codes with shape (B, D, T), where B is the batch size, D is the dimension of the codes, and T is the temporal dimension.
  • Return type: Tensor

################### Examples

>>> model = ESPnetGANCodecModel(codec)
>>> audio_input = torch.randn(2, 16000)  # Simulated audio
>>> codes = model.encode_continuous(audio_input)
>>> print(codes.shape)  # Output shape will be (2, D, T)

############ NOTE The method will automatically reshape the input tensor to ensure it has the appropriate dimensions for encoding.

forward(audio: Tensor, forward_generator: bool = True, **kwargs) → Dict[str, Any]

Return generator or discriminator loss in a dictionary format.

This method processes the input audio through the GAN codec model and returns the computed loss along with various statistics. Depending on the forward_generator flag, it can either compute the generator’s loss or the discriminator’s loss.

  • Parameters:
    • audio (Tensor) – Audio waveform tensor of shape (B, T_wav), where B is the batch size and T_wav is the number of audio samples.
    • forward_generator (bool) – Flag indicating whether to forward the generator. If True, the generator’s loss is computed; if False, the discriminator’s loss is computed.
    • kwargs – Additional keyword arguments. The “utt_id” should be among the input if required.
  • Returns:
    • loss (Tensor): Loss scalar tensor indicating the computed loss.
    • stats (Dict[str, float]): Dictionary of statistics to be monitored during training.
    • weight (Tensor): Weight tensor used to summarize losses.
    • optim_idx (int): Optimizer index (0 for G and 1 for D) indicating which model’s parameters should be updated.
  • Return type: Dict[str, Any]

################### Examples

>>> model = ESPnetGANCodecModel(codec)
>>> audio_input = torch.randn(8, 16000)  # Example input tensor
>>> result = model.forward(audio_input, forward_generator=True)
>>> print(result['loss'])  # Accessing the computed loss

############ NOTE Ensure that the codec object passed during model initialization has the required generator and discriminator attributes.

  • Raises:
    • AssertionError – If the codec does not have the required attributes
    • or if any input is invalid. –

meta_info() → Dict[str, Any]

Return meta information of the codec.

This method retrieves and returns the meta information associated with the codec being used in the ESPnetGANCodecModel. The meta information typically includes details such as the codec’s architecture, configuration, and other relevant attributes.

  • Returns: A dictionary containing the meta information of the codec.
  • Return type: Dict[str, Any]

################### Examples

>>> model = ESPnetGANCodecModel(codec=my_codec)
>>> info = model.meta_info()
>>> print(info)
{'architecture': 'GAN', 'version': '1.0', ...}

############ NOTE Ensure that the codec has a properly defined meta_info method to retrieve the necessary information.

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