Metadata-Version: 2.1
Name: paddlelibrosa
Version: 0.0.1
Summary: Paddle implemention of part of librosa functions.
Home-page: https://github.com/agentmaker/paddlelibrosa
License: UNKNOWN
Description: # Paddle-Librosa: Paddle implementation of Librosa
        
        This codebase provides Paddle implementation of some librosa functions. If users previously used for training cpu-extracted features from librosa, but want to add GPU acceleration during training and evaluation, Paddle-Librosa will provide almost identical features to standard paddlelibrosa functions (numerical difference less than 1e-5).
        
        ## Install
        ```bash
        $ git clone https://github.com/AgentMaker/Paddle-Librosa.git
        $ pip install Paddle-Librosa/
        ```
        
        ## Examples 1
        
        Extract Log mel spectrogram with Paddle-Librosa.
        
        ```python
        import paddle
        import paddlelibrosa as pl
        
        batch_size = 16
        sample_rate = 22050
        win_length = 2048
        hop_length = 512
        n_mels = 128
        
        batch_audio = paddle.uniform((batch_size, sample_rate))  # (batch_size, sample_rate)
        
        # Paddle-Librosa feature extractor the same as librosa.feature.melspectrogram()
        feature_extractor = paddle.nn.Sequential(
            pl.Spectrogram(
                hop_length=hop_length,
                win_length=win_length,
            ), pl.LogmelFilterBank(
                sr=sample_rate,
                n_mels=n_mels,
                is_log=False, # Default is true
            ))
        batch_feature = feature_extractor(batch_audio) # (batch_size, 1, time_steps, mel_bins)
        ```
        
        ## Examples 2
        
        Extracting spectrogram, then log mel spectrogram, STFT and ISTFT with Paddle-Librosa.
        
        ```python
        import paddle
        import paddlelibrosa as pl
        
        batch_size = 16
        sample_rate = 22050
        win_length = 2048
        hop_length = 512
        n_mels = 128
        
        batch_audio = paddle.empty(batch_size, sample_rate).uniform_(-1, 1)  # (batch_size, sample_rate)
        
        # Spectrogram
        spectrogram_extractor = pl.Spectrogram(n_fft=win_length, hop_length=hop_length)
        sp = spectrogram_extractor.forward(batch_audio)   # (batch_size, 1, time_steps, freq_bins)
        
        # Log mel spectrogram
        logmel_extractor = pl.LogmelFilterBank(sr=sample_rate, n_fft=win_length, n_mels=n_mels)
        logmel = logmel_extractor.forward(sp)   # (batch_size, 1, time_steps, mel_bins)
        
        # STFT
        stft_extractor = pl.STFT(n_fft=win_length, hop_length=hop_length)
        (real, imag) = stft_extractor.forward(batch_audio)
        # real: (batch_size, 1, time_steps, freq_bins), imag: (batch_size, 1, time_steps, freq_bins) #
        
        # ISTFT
        istft_extractor = pl.ISTFT(n_fft=win_length, hop_length=hop_length)
        y = istft_extractor.forward(real, imag, length=batch_audio.shape[-1])    # (batch_size, samples_num)
        ```
        
        ## External links
        Other related repos include:
        
        torchlibrosa: https://github.com/qiuqiangkong/torchlibrosa
        
        ## Contact us
        Email : [agentmaker@163.com]()<br>
        QQ Group : 1005109853
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
