fix loading multi-channel mp3 file bug

haoneng.lhn

2023-06-27 a0defaf40e62417a76b954819dc8119290ea9ab1

fix loading multi-channel mp3 file bug

 funasr/bin/asr_inference_launch.py

@@ -867,7 +867,10 @@
            try:
                raw_inputs = torchaudio.load(data_path_and_name_and_type[0])[0][0]
            except:
                raw_inputs = torch.tensor(soundfile.read(data_path_and_name_and_type[0])[0])
                raw_inputs = soundfile.read(data_path_and_name_and_type[0], dtype='float32')[0]
                if raw_inputs.ndim == 2:
                    raw_inputs = raw_inputs[:, 0]
                raw_inputs = torch.tensor(raw_inputs)
        if data_path_and_name_and_type is None and raw_inputs is not None:
            if isinstance(raw_inputs, np.ndarray):
                raw_inputs = torch.tensor(raw_inputs)

 funasr/datasets/iterable_dataset.py

@@ -71,7 +71,10 @@
    try:
        return torchaudio.load(input)[0].numpy()
    except:
        return np.expand_dims(soundfile.read(input)[0], axis=0)
        waveform, _ = soundfile.read(input, dtype='float32')
        if waveform.ndim == 2:
            waveform = waveform[:, 0]
        return np.expand_dims(waveform, axis=0)

DATA_TYPES = {
    "sound": load_wav,

 funasr/datasets/large_datasets/dataset.py

@@ -128,7 +128,9 @@
                        try:
                            waveform, sampling_rate = torchaudio.load(path)
                        except:
                            waveform, sampling_rate = soundfile.read(path)
                            waveform, sampling_rate = soundfile.read(path, dtype='float32')
                            if waveform.ndim == 2:
                                waveform = waveform[:, 0]
                            waveform = np.expand_dims(waveform, axis=0)
                            waveform = torch.tensor(waveform)
                        if self.frontend_conf is not None:

 funasr/utils/wav_utils.py

@@ -166,7 +166,9 @@
        try:
            waveform, audio_sr = torchaudio.load(wav_file)
        except:
            waveform, audio_sr = soundfile.read(wav_file)
            waveform, audio_sr = soundfile.read(wav_file, dtype='float32')
            if waveform.ndim == 2:
                waveform = waveform[:, 0]
            waveform = torch.tensor(np.expand_dims(waveform, axis=0))
        waveform = waveform * (1 << 15)
        waveform = torch_resample(waveform, audio_sr, model_sr)