auto apply_effects_file()

in torchaudio/csrc/sox/effects.cpp [92:138]

• 36 lines of code
• 3 McCabe index (conditional complexity)

auto apply_effects_file(
    const std::string& path,
    const std::vector<std::vector<std::string>>& effects,
    c10::optional<bool> normalize,
    c10::optional<bool> channels_first,
    const c10::optional<std::string>& format)
    -> std::tuple<torch::Tensor, int64_t> {
  // Open input file
  SoxFormat sf(sox_open_read(
      path.c_str(),
      /*signal=*/nullptr,
      /*encoding=*/nullptr,
      /*filetype=*/format.has_value() ? format.value().c_str() : nullptr));

  validate_input_file(sf, path);

  const auto dtype = get_dtype(sf->encoding.encoding, sf->signal.precision);

  // Prepare output
  std::vector<sox_sample_t> out_buffer;
  out_buffer.reserve(sf->signal.length);

  // Create and run SoxEffectsChain
  torchaudio::sox_effects_chain::SoxEffectsChain chain(
      /*input_encoding=*/sf->encoding,
      /*output_encoding=*/get_tensor_encodinginfo(dtype));

  chain.addInputFile(sf);
  for (const auto& effect : effects) {
    chain.addEffect(effect);
  }
  chain.addOutputBuffer(&out_buffer);
  chain.run();

  // Create tensor from buffer
  bool channels_first_ = channels_first.value_or(true);
  auto tensor = convert_to_tensor(
      /*buffer=*/out_buffer.data(),
      /*num_samples=*/out_buffer.size(),
      /*num_channels=*/chain.getOutputNumChannels(),
      dtype,
      normalize.value_or(true),
      channels_first_);

  return std::tuple<torch::Tensor, int64_t>(
      tensor, chain.getOutputSampleRate());
}