refactor(audio): improve type hints, address lint issues

TTS/utils/audio/numpy_transforms.py

@@ -1,6 +1,6 @@
 import logging
 from io import BytesIO
-from typing import Tuple
+from typing import Optional
 
 import librosa
 import numpy as np
@@ -16,11 +16,11 @@ logger = logging.getLogger(__name__)
 
 def build_mel_basis(
     *,
-    sample_rate: int = None,
-    fft_size: int = None,
-    num_mels: int = None,
-    mel_fmax: int = None,
-    mel_fmin: int = None,
+    sample_rate: int,
+    fft_size: int,
+    num_mels: int,
+    mel_fmin: int,
+    mel_fmax: Optional[int] = None,
     **kwargs,
 ) -> np.ndarray:
     """Build melspectrogram basis.
@@ -34,9 +34,7 @@ def build_mel_basis(
     return librosa.filters.mel(sr=sample_rate, n_fft=fft_size, n_mels=num_mels, fmin=mel_fmin, fmax=mel_fmax)
 
 
-def millisec_to_length(
-    *, frame_length_ms: int = None, frame_shift_ms: int = None, sample_rate: int = None, **kwargs
-) -> Tuple[int, int]:
+def millisec_to_length(*, frame_length_ms: float, frame_shift_ms: float, sample_rate: int, **kwargs) -> tuple[int, int]:
     """Compute hop and window length from milliseconds.
 
     Returns:
@@ -61,7 +59,7 @@ def _exp(x, base):
     return np.exp(x)
 
 
-def amp_to_db(*, x: np.ndarray = None, gain: float = 1, base: int = 10, **kwargs) -> np.ndarray:
+def amp_to_db(*, x: np.ndarray, gain: float = 1, base: int = 10, **kwargs) -> np.ndarray:
     """Convert amplitude values to decibels.
 
     Args:
@@ -77,7 +75,7 @@ def amp_to_db(*, x: np.ndarray = None, gain: float = 1, base: int = 10, **kwargs
 
 
 # pylint: disable=no-self-use
-def db_to_amp(*, x: np.ndarray = None, gain: float = 1, base: int = 10, **kwargs) -> np.ndarray:
+def db_to_amp(*, x: np.ndarray, gain: float = 1, base: float = 10, **kwargs) -> np.ndarray:
     """Convert decibels spectrogram to amplitude spectrogram.
 
     Args:
@@ -104,18 +102,20 @@ def preemphasis(*, x: np.ndarray, coef: float = 0.97, **kwargs) -> np.ndarray:
         np.ndarray: Decorrelated audio signal.
     """
     if coef == 0:
-        raise RuntimeError(" [!] Preemphasis is set 0.0.")
+        msg = " [!] Preemphasis is set 0.0."
+        raise RuntimeError(msg)
     return scipy.signal.lfilter([1, -coef], [1], x)
 
 
-def deemphasis(*, x: np.ndarray = None, coef: float = 0.97, **kwargs) -> np.ndarray:
+def deemphasis(*, x: np.ndarray, coef: float = 0.97, **kwargs) -> np.ndarray:
     """Reverse pre-emphasis."""
     if coef == 0:
-        raise RuntimeError(" [!] Preemphasis is set 0.0.")
+        msg = " [!] Preemphasis is set 0.0."
+        raise ValueError(msg)
     return scipy.signal.lfilter([1], [1, -coef], x)
 
 
-def spec_to_mel(*, spec: np.ndarray, mel_basis: np.ndarray = None, **kwargs) -> np.ndarray:
+def spec_to_mel(*, spec: np.ndarray, mel_basis: np.ndarray, **kwargs) -> np.ndarray:
     """Convert a full scale linear spectrogram output of a network to a melspectrogram.
 
     Args:
@@ -130,14 +130,14 @@ def spec_to_mel(*, spec: np.ndarray, mel_basis: np.ndarray = None, **kwargs) ->
     return np.dot(mel_basis, spec)
 
 
-def mel_to_spec(*, mel: np.ndarray = None, mel_basis: np.ndarray = None, **kwargs) -> np.ndarray:
+def mel_to_spec(*, mel: np.ndarray, mel_basis: np.ndarray, **kwargs) -> np.ndarray:
     """Convert a melspectrogram to full scale spectrogram."""
     assert (mel < 0).sum() == 0, " [!] Input values must be non-negative."
     inv_mel_basis = np.linalg.pinv(mel_basis)
     return np.maximum(1e-10, np.dot(inv_mel_basis, mel))
 
 
-def wav_to_spec(*, wav: np.ndarray = None, **kwargs) -> np.ndarray:
+def wav_to_spec(*, wav: np.ndarray, **kwargs) -> np.ndarray:
     """Compute a spectrogram from a waveform.
 
     Args:
@@ -151,7 +151,7 @@ def wav_to_spec(*, wav: np.ndarray = None, **kwargs) -> np.ndarray:
     return S.astype(np.float32)
 
 
-def wav_to_mel(*, wav: np.ndarray = None, mel_basis=None, **kwargs) -> np.ndarray:
+def wav_to_mel(*, wav: np.ndarray, mel_basis: np.ndarray, **kwargs) -> np.ndarray:
     """Compute a melspectrogram from a waveform."""
     D = stft(y=wav, **kwargs)
     S = spec_to_mel(spec=np.abs(D), mel_basis=mel_basis, **kwargs)
@@ -164,20 +164,20 @@ def spec_to_wav(*, spec: np.ndarray, power: float = 1.5, **kwargs) -> np.ndarray
     return griffin_lim(spec=S**power, **kwargs)
 
 
-def mel_to_wav(*, mel: np.ndarray = None, power: float = 1.5, **kwargs) -> np.ndarray:
+def mel_to_wav(*, mel: np.ndarray, mel_basis: np.ndarray, power: float = 1.5, **kwargs) -> np.ndarray:
     """Convert a melspectrogram to a waveform using Griffi-Lim vocoder."""
     S = mel.copy()
-    S = mel_to_spec(mel=S, mel_basis=kwargs["mel_basis"])  # Convert back to linear
+    S = mel_to_spec(mel=S, mel_basis=mel_basis)  # Convert back to linear
     return griffin_lim(spec=S**power, **kwargs)
 
 
 ### STFT and ISTFT ###
 def stft(
     *,
-    y: np.ndarray = None,
-    fft_size: int = None,
-    hop_length: int = None,
-    win_length: int = None,
+    y: np.ndarray,
+    fft_size: int,
+    hop_length: Optional[int] = None,
+    win_length: Optional[int] = None,
     pad_mode: str = "reflect",
     window: str = "hann",
     center: bool = True,
@@ -203,9 +203,9 @@ def stft(
 
 def istft(
     *,
-    y: np.ndarray = None,
-    hop_length: int = None,
-    win_length: int = None,
+    y: np.ndarray,
+    hop_length: Optional[int] = None,
+    win_length: Optional[int] = None,
     window: str = "hann",
     center: bool = True,
     **kwargs,
@@ -220,7 +220,7 @@ def istft(
     return librosa.istft(y, hop_length=hop_length, win_length=win_length, center=center, window=window)
 
 
-def griffin_lim(*, spec: np.ndarray = None, num_iter=60, **kwargs) -> np.ndarray:
+def griffin_lim(*, spec: np.ndarray, num_iter=60, **kwargs) -> np.ndarray:
     angles = np.exp(2j * np.pi * np.random.rand(*spec.shape))
     S_complex = np.abs(spec).astype(complex)
     y = istft(y=S_complex * angles, **kwargs)
@@ -233,11 +233,11 @@ def griffin_lim(*, spec: np.ndarray = None, num_iter=60, **kwargs) -> np.ndarray
     return y
 
 
-def compute_stft_paddings(
-    *, x: np.ndarray = None, hop_length: int = None, pad_two_sides: bool = False, **kwargs
-) -> Tuple[int, int]:
-    """Compute paddings used by Librosa's STFT. Compute right padding (final frame) or both sides padding
-    (first and final frames)"""
+def compute_stft_paddings(*, x: np.ndarray, hop_length: int, pad_two_sides: bool = False, **kwargs) -> tuple[int, int]:
+    """Compute paddings used by Librosa's STFT.
+
+    Compute right padding (final frame) or both sides padding (first and final frames).
+    """
     pad = (x.shape[0] // hop_length + 1) * hop_length - x.shape[0]
     if not pad_two_sides:
         return 0, pad
@@ -246,12 +246,12 @@ def compute_stft_paddings(
 
 def compute_f0(
     *,
-    x: np.ndarray = None,
-    pitch_fmax: float = None,
-    pitch_fmin: float = None,
-    hop_length: int = None,
-    win_length: int = None,
-    sample_rate: int = None,
+    x: np.ndarray,
+    pitch_fmax: Optional[float] = None,
+    pitch_fmin: Optional[float] = None,
+    hop_length: int,
+    win_length: int,
+    sample_rate: int,
     stft_pad_mode: str = "reflect",
     center: bool = True,
     **kwargs,
@@ -323,19 +323,18 @@ def compute_energy(y: np.ndarray, **kwargs) -> np.ndarray:
     """
     x = stft(y=y, **kwargs)
     mag, _ = magphase(x)
-    energy = np.sqrt(np.sum(mag**2, axis=0))
-    return energy
+    return np.sqrt(np.sum(mag**2, axis=0))
 
 
 ### Audio Processing ###
 def find_endpoint(
     *,
-    wav: np.ndarray = None,
+    wav: np.ndarray,
     trim_db: float = -40,
-    sample_rate: int = None,
-    min_silence_sec=0.8,
-    gain: float = None,
-    base: int = None,
+    sample_rate: int,
+    min_silence_sec: float = 0.8,
+    gain: float = 1,
+    base: float = 10,
     **kwargs,
 ) -> int:
     """Find the last point without silence at the end of a audio signal.
@@ -344,8 +343,8 @@ def find_endpoint(
         wav (np.ndarray): Audio signal.
         threshold_db (int, optional): Silence threshold in decibels. Defaults to -40.
         min_silence_sec (float, optional): Ignore silences that are shorter then this in secs. Defaults to 0.8.
-        gian (float, optional): Gain to be used to convert trim_db to trim_amp. Defaults to None.
-        base (int, optional): Base of the logarithm used to convert trim_db to trim_amp. Defaults to 10.
+        gain (float, optional): Gain factor to be used to convert trim_db to trim_amp. Defaults to 1.
+        base (float, optional): Base of the logarithm used to convert trim_db to trim_amp. Defaults to 10.
 
     Returns:
         int: Last point without silence.
@@ -361,20 +360,20 @@ def find_endpoint(
 
 def trim_silence(
     *,
-    wav: np.ndarray = None,
-    sample_rate: int = None,
-    trim_db: float = None,
-    win_length: int = None,
-    hop_length: int = None,
+    wav: np.ndarray,
+    sample_rate: int,
+    trim_db: float = 60,
+    win_length: int,
+    hop_length: int,
     **kwargs,
 ) -> np.ndarray:
-    """Trim silent parts with a threshold and 0.01 sec margin"""
+    """Trim silent parts with a threshold and 0.01 sec margin."""
     margin = int(sample_rate * 0.01)
     wav = wav[margin:-margin]
     return librosa.effects.trim(wav, top_db=trim_db, frame_length=win_length, hop_length=hop_length)[0]
 
 
-def volume_norm(*, x: np.ndarray = None, coef: float = 0.95, **kwargs) -> np.ndarray:
+def volume_norm(*, x: np.ndarray, coef: float = 0.95, **kwargs) -> np.ndarray:
     """Normalize the volume of an audio signal.
 
     Args:
@@ -387,7 +386,7 @@ def volume_norm(*, x: np.ndarray = None, coef: float = 0.95, **kwargs) -> np.nda
     return x / abs(x).max() * coef
 
 
-def rms_norm(*, wav: np.ndarray = None, db_level: float = -27.0, **kwargs) -> np.ndarray:
+def rms_norm(*, wav: np.ndarray, db_level: float = -27.0, **kwargs) -> np.ndarray:
     r = 10 ** (db_level / 20)
     a = np.sqrt((len(wav) * (r**2)) / np.sum(wav**2))
     return wav * a
@@ -404,11 +403,10 @@ def rms_volume_norm(*, x: np.ndarray, db_level: float = -27.0, **kwargs) -> np.n
         np.ndarray: RMS normalized waveform.
     """
     assert -99 <= db_level <= 0, " [!] db_level should be between -99 and 0"
-    wav = rms_norm(wav=x, db_level=db_level)
-    return wav
+    return rms_norm(wav=x, db_level=db_level)
 
 
-def load_wav(*, filename: str, sample_rate: int = None, resample: bool = False, **kwargs) -> np.ndarray:
+def load_wav(*, filename: str, sample_rate: Optional[int] = None, resample: bool = False, **kwargs) -> np.ndarray:
     """Read a wav file using Librosa and optionally resample, silence trim, volume normalize.
 
     Resampling slows down loading the file significantly. Therefore it is recommended to resample the file before.
@@ -433,13 +431,13 @@ def load_wav(*, filename: str, sample_rate: int = None, resample: bool = False,
     return x
 
 
-def save_wav(*, wav: np.ndarray, path: str, sample_rate: int = None, pipe_out=None, **kwargs) -> None:
+def save_wav(*, wav: np.ndarray, path: str, sample_rate: int, pipe_out=None, **kwargs) -> None:
     """Save float waveform to a file using Scipy.
 
     Args:
         wav (np.ndarray): Waveform with float values in range [-1, 1] to save.
         path (str): Path to a output file.
-        sr (int, optional): Sampling rate used for saving to the file. Defaults to None.
+        sr (int): Sampling rate used for saving to the file. Defaults to None.
         pipe_out (BytesIO, optional): Flag to stdout the generated TTS wav file for shell pipe.
     """
     wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
@@ -465,8 +463,7 @@ def mulaw_encode(*, wav: np.ndarray, mulaw_qc: int, **kwargs) -> np.ndarray:
 def mulaw_decode(*, wav, mulaw_qc: int, **kwargs) -> np.ndarray:
     """Recovers waveform from quantized values."""
     mu = 2**mulaw_qc - 1
-    x = np.sign(wav) / mu * ((1 + mu) ** np.abs(wav) - 1)
-    return x
+    return np.sign(wav) / mu * ((1 + mu) ** np.abs(wav) - 1)
 
 
 def encode_16bits(*, x: np.ndarray, **kwargs) -> np.ndarray:

TTS/utils/audio/processor.py

@@ -1,6 +1,6 @@
 import logging
 from io import BytesIO
-from typing import Dict, Tuple
+from typing import Optional
 
 import librosa
 import numpy as np
@@ -32,7 +32,7 @@ logger = logging.getLogger(__name__)
 # pylint: disable=too-many-public-methods
 
 
-class AudioProcessor(object):
+class AudioProcessor:
     """Audio Processor for TTS.
 
     Note:
@@ -172,7 +172,7 @@ class AudioProcessor(object):
         db_level=None,
         stats_path=None,
         **_,
-    ):
+    ) -> None:
         # setup class attributed
         self.sample_rate = sample_rate
         self.resample = resample
@@ -210,7 +210,8 @@ class AudioProcessor(object):
         elif log_func == "np.log10":
             self.base = 10
         else:
-            raise ValueError(" [!] unknown `log_func` value.")
+            msg = " [!] unknown `log_func` value."
+            raise ValueError(msg)
         # setup stft parameters
         if hop_length is None:
             # compute stft parameters from given time values
@@ -254,7 +255,7 @@ class AudioProcessor(object):
 
     ### normalization ###
     def normalize(self, S: np.ndarray) -> np.ndarray:
-        """Normalize values into `[0, self.max_norm]` or `[-self.max_norm, self.max_norm]`
+        """Normalize values into `[0, self.max_norm]` or `[-self.max_norm, self.max_norm]`.
 
         Args:
             S (np.ndarray): Spectrogram to normalize.
@@ -272,10 +273,10 @@ class AudioProcessor(object):
             if hasattr(self, "mel_scaler"):
                 if S.shape[0] == self.num_mels:
                     return self.mel_scaler.transform(S.T).T
-                elif S.shape[0] == self.fft_size / 2:
+                if S.shape[0] == self.fft_size / 2:
                     return self.linear_scaler.transform(S.T).T
-                else:
-                    raise RuntimeError(" [!] Mean-Var stats does not match the given feature dimensions.")
+                msg = " [!] Mean-Var stats does not match the given feature dimensions."
+                raise RuntimeError(msg)
             # range normalization
             S -= self.ref_level_db  # discard certain range of DB assuming it is air noise
             S_norm = (S - self.min_level_db) / (-self.min_level_db)
@@ -286,13 +287,11 @@ class AudioProcessor(object):
                         S_norm, -self.max_norm, self.max_norm  # pylint: disable=invalid-unary-operand-type
                     )
                 return S_norm
-            else:
-                S_norm = self.max_norm * S_norm
-                if self.clip_norm:
-                    S_norm = np.clip(S_norm, 0, self.max_norm)
-                return S_norm
-        else:
-            return S
+            S_norm = self.max_norm * S_norm
+            if self.clip_norm:
+                S_norm = np.clip(S_norm, 0, self.max_norm)
+            return S_norm
+        return S
 
     def denormalize(self, S: np.ndarray) -> np.ndarray:
         """Denormalize spectrogram values.
@@ -313,10 +312,10 @@ class AudioProcessor(object):
             if hasattr(self, "mel_scaler"):
                 if S_denorm.shape[0] == self.num_mels:
                     return self.mel_scaler.inverse_transform(S_denorm.T).T
-                elif S_denorm.shape[0] == self.fft_size / 2:
+                if S_denorm.shape[0] == self.fft_size / 2:
                     return self.linear_scaler.inverse_transform(S_denorm.T).T
-                else:
-                    raise RuntimeError(" [!] Mean-Var stats does not match the given feature dimensions.")
+                msg = " [!] Mean-Var stats does not match the given feature dimensions."
+                raise RuntimeError(msg)
             if self.symmetric_norm:
                 if self.clip_norm:
                     S_denorm = np.clip(
@@ -324,16 +323,14 @@ class AudioProcessor(object):
                     )
                 S_denorm = ((S_denorm + self.max_norm) * -self.min_level_db / (2 * self.max_norm)) + self.min_level_db
                 return S_denorm + self.ref_level_db
-            else:
-                if self.clip_norm:
-                    S_denorm = np.clip(S_denorm, 0, self.max_norm)
-                S_denorm = (S_denorm * -self.min_level_db / self.max_norm) + self.min_level_db
-                return S_denorm + self.ref_level_db
-        else:
-            return S_denorm
+            if self.clip_norm:
+                S_denorm = np.clip(S_denorm, 0, self.max_norm)
+            S_denorm = (S_denorm * -self.min_level_db / self.max_norm) + self.min_level_db
+            return S_denorm + self.ref_level_db
+        return S_denorm
 
     ### Mean-STD scaling ###
-    def load_stats(self, stats_path: str) -> Tuple[np.array, np.array, np.array, np.array, Dict]:
+    def load_stats(self, stats_path: str) -> tuple[np.array, np.array, np.array, np.array, dict]:
         """Loading mean and variance statistics from a `npy` file.
 
         Args:
@@ -351,7 +348,7 @@ class AudioProcessor(object):
         stats_config = stats["audio_config"]
         # check all audio parameters used for computing stats
         skip_parameters = ["griffin_lim_iters", "stats_path", "do_trim_silence", "ref_level_db", "power"]
-        for key in stats_config.keys():
+        for key in stats_config:
             if key in skip_parameters:
                 continue
             if key not in ["sample_rate", "trim_db"]:
@@ -415,10 +412,7 @@ class AudioProcessor(object):
             win_length=self.win_length,
             pad_mode=self.stft_pad_mode,
         )
-        if self.do_amp_to_db_linear:
-            S = amp_to_db(x=np.abs(D), gain=self.spec_gain, base=self.base)
-        else:
-            S = np.abs(D)
+        S = amp_to_db(x=np.abs(D), gain=self.spec_gain, base=self.base) if self.do_amp_to_db_linear else np.abs(D)
         return self.normalize(S).astype(np.float32)
 
     def melspectrogram(self, y: np.ndarray) -> np.ndarray:
@@ -467,8 +461,7 @@ class AudioProcessor(object):
         S = db_to_amp(x=S, gain=self.spec_gain, base=self.base)
         S = spec_to_mel(spec=np.abs(S), mel_basis=self.mel_basis)
         S = amp_to_db(x=S, gain=self.spec_gain, base=self.base)
-        mel = self.normalize(S)
-        return mel
+        return self.normalize(S)
 
     def _griffin_lim(self, S):
         return griffin_lim(
@@ -502,7 +495,7 @@ class AudioProcessor(object):
         if len(x) % self.hop_length == 0:
             x = np.pad(x, (0, self.hop_length // 2), mode=self.stft_pad_mode)
 
-        f0 = compute_f0(
+        return compute_f0(
             x=x,
             pitch_fmax=self.pitch_fmax,
             pitch_fmin=self.pitch_fmin,
@@ -513,8 +506,6 @@ class AudioProcessor(object):
             center=True,
         )
 
-        return f0
-
     ### Audio Processing ###
     def find_endpoint(self, wav: np.ndarray, min_silence_sec=0.8) -> int:
         """Find the last point without silence at the end of a audio signal.
@@ -537,7 +528,7 @@ class AudioProcessor(object):
         )
 
     def trim_silence(self, wav):
-        """Trim silent parts with a threshold and 0.01 sec margin"""
+        """Trim silent parts with a threshold and 0.01 sec margin."""
         return trim_silence(
             wav=wav,
             sample_rate=self.sample_rate,
@@ -572,7 +563,7 @@ class AudioProcessor(object):
         return rms_volume_norm(x=x, db_level=db_level)
 
     ### save and load ###
-    def load_wav(self, filename: str, sr: int = None) -> np.ndarray:
+    def load_wav(self, filename: str, sr: Optional[int] = None) -> np.ndarray:
         """Read a wav file using Librosa and optionally resample, silence trim, volume normalize.
 
         Resampling slows down loading the file significantly. Therefore it is recommended to resample the file before.