Create spectro.py

spectro.py

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+"""
+Audio processing tools to convert between spectrogram images and waveforms.
+"""
+import io
+import typing as T
+
+import numpy as np
+from PIL import Image
+import pydub
+from scipy.io import wavfile
+import torch
+import torchaudio
+
+
+def wav_bytes_from_spectrogram_image(image: Image.Image) -> T.Tuple[io.BytesIO, float]:
+    """
+    Reconstruct a WAV audio clip from a spectrogram image. Also returns the duration in seconds.
+    """
+
+    max_volume = 50
+    power_for_image = 0.25
+    Sxx = spectrogram_from_image(image, max_volume=max_volume, power_for_image=power_for_image)
+
+    sample_rate = 44100  # [Hz]
+    clip_duration_ms = 5000  # [ms]
+
+    bins_per_image = 512
+    n_mels = 512
+
+    # FFT parameters
+    window_duration_ms = 100  # [ms]
+    padded_duration_ms = 400  # [ms]
+    step_size_ms = 10  # [ms]
+
+    # Derived parameters
+    num_samples = int(image.width / float(bins_per_image) * clip_duration_ms) * sample_rate
+    n_fft = int(padded_duration_ms / 1000.0 * sample_rate)
+    hop_length = int(step_size_ms / 1000.0 * sample_rate)
+    win_length = int(window_duration_ms / 1000.0 * sample_rate)
+
+    samples = waveform_from_spectrogram(
+        Sxx=Sxx,
+        n_fft=n_fft,
+        hop_length=hop_length,
+        win_length=win_length,
+        num_samples=num_samples,
+        sample_rate=sample_rate,
+        mel_scale=True,
+        n_mels=n_mels,
+        max_mel_iters=200,
+        num_griffin_lim_iters=32,
+    )
+
+    wav_bytes = io.BytesIO()
+    wavfile.write(wav_bytes, sample_rate, samples.astype(np.int16))
+    wav_bytes.seek(0)
+
+    duration_s = float(len(samples)) / sample_rate
+
+    return wav_bytes, duration_s
+
+
+def spectrogram_from_image(
+    image: Image.Image, max_volume: float = 50, power_for_image: float = 0.25
+) -> np.ndarray:
+    """
+    Compute a spectrogram magnitude array from a spectrogram image.
+
+    TODO(hayk): Add image_from_spectrogram and call this out as the reverse.
+    """
+    # Convert to a numpy array of floats
+    data = np.array(image).astype(np.float32)
+
+    # Flip Y take a single channel
+    data = data[::-1, :, 0]
+
+    # Invert
+    data = 255 - data
+
+    # Rescale to max volume
+    data = data * max_volume / 255
+
+    # Reverse the power curve
+    data = np.power(data, 1 / power_for_image)
+
+    return data
+
+
+def spectrogram_from_waveform(
+    waveform: np.ndarray,
+    sample_rate: int,
+    n_fft: int,
+    hop_length: int,
+    win_length: int,
+    mel_scale: bool = True,
+    n_mels: int = 512,
+) -> np.ndarray:
+    """
+    Compute a spectrogram from a waveform.
+    """
+
+    spectrogram_func = torchaudio.transforms.Spectrogram(
+        n_fft=n_fft,
+        power=None,
+        hop_length=hop_length,
+        win_length=win_length,
+    )
+
+    waveform_tensor = torch.from_numpy(waveform.astype(np.float32)).reshape(1, -1)
+    Sxx_complex = spectrogram_func(waveform_tensor).numpy()[0]
+
+    Sxx_mag = np.abs(Sxx_complex)
+
+    if mel_scale:
+        mel_scaler = torchaudio.transforms.MelScale(
+            n_mels=n_mels,
+            sample_rate=sample_rate,
+            f_min=0,
+            f_max=10000,
+            n_stft=n_fft // 2 + 1,
+            norm=None,
+            mel_scale="htk",
+        )
+
+        Sxx_mag = mel_scaler(torch.from_numpy(Sxx_mag)).numpy()
+
+    return Sxx_mag
+
+
+def waveform_from_spectrogram(
+    Sxx: np.ndarray,
+    n_fft: int,
+    hop_length: int,
+    win_length: int,
+    num_samples: int,
+    sample_rate: int,
+    mel_scale: bool = True,
+    n_mels: int = 512,
+    max_mel_iters: int = 200,
+    num_griffin_lim_iters: int = 32,
+    device: str = "cuda:0",
+) -> np.ndarray:
+    """
+    Reconstruct a waveform from a spectrogram.
+
+    This is an approximate inverse of spectrogram_from_waveform, using the Griffin-Lim algorithm
+    to approximate the phase.
+    """
+    Sxx_torch = torch.from_numpy(Sxx).to(device)
+
+    # TODO(hayk): Make this a class that caches the two things
+
+    if mel_scale:
+        mel_inv_scaler = torchaudio.transforms.InverseMelScale(
+            n_mels=n_mels,
+            sample_rate=sample_rate,
+            f_min=0,
+            f_max=10000,
+            n_stft=n_fft // 2 + 1,
+            norm=None,
+            mel_scale="htk",
+            max_iter=max_mel_iters,
+        ).to(device)
+
+        Sxx_torch = mel_inv_scaler(Sxx_torch)
+
+    griffin_lim = torchaudio.transforms.GriffinLim(
+        n_fft=n_fft,
+        win_length=win_length,
+        hop_length=hop_length,
+        power=1.0,
+        n_iter=num_griffin_lim_iters,
+    ).to(device)
+
+    waveform = griffin_lim(Sxx_torch).cpu().numpy()
+
+    return waveform
+
+
+def mp3_bytes_from_wav_bytes(wav_bytes: io.BytesIO) -> io.BytesIO:
+    mp3_bytes = io.BytesIO()
+    sound = pydub.AudioSegment.from_wav(wav_bytes)
+    sound.export(mp3_bytes, format="mp3")
+    mp3_bytes.seek(0)
+    return mp3_bytes