refactor(audio): remove duplicate save_wav code

TTS/utils/audio/numpy_transforms.py

@@ -431,7 +431,16 @@ def load_wav(*, filename: str, sample_rate: Optional[int] = None, resample: bool
     return x
 
 
-def save_wav(*, wav: np.ndarray, path: str, sample_rate: int, pipe_out=None, **kwargs) -> None:
+def save_wav(
+    *,
+    wav: np.ndarray,
+    path: str,
+    sample_rate: int,
+    pipe_out=None,
+    do_rms_norm: bool = False,
+    db_level: float = -27.0,
+    **kwargs,
+) -> None:
     """Save float waveform to a file using Scipy.
 
     Args:
@@ -439,8 +448,16 @@ def save_wav(*, wav: np.ndarray, path: str, sample_rate: int, pipe_out=None, **k
         path (str): Path to a output file.
         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.
+        do_rms_norm (bool): Whether to apply RMS normalization
+        db_level (float): Target dB level in RMS.
     """
-    wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
+    if do_rms_norm:
+        if db_level is None:
+            msg = "`db_level` cannot be None with `do_rms_norm=True`"
+            raise ValueError(msg)
+        wav_norm = rms_volume_norm(x=wav, db_level=db_level)
+    else:
+        wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
 
     wav_norm = wav_norm.astype(np.int16)
     if pipe_out:

TTS/utils/audio/processor.py

@@ -1,11 +1,8 @@
 import logging
-from io import BytesIO
 from typing import Optional
 
 import librosa
 import numpy as np
-import scipy.io.wavfile
-import scipy.signal
 
 from TTS.tts.utils.helpers import StandardScaler
 from TTS.utils.audio.numpy_transforms import (
@@ -21,6 +18,7 @@ from TTS.utils.audio.numpy_transforms import (
     millisec_to_length,
     preemphasis,
     rms_volume_norm,
+    save_wav,
     spec_to_mel,
     stft,
     trim_silence,
@@ -590,7 +588,7 @@ class AudioProcessor:
             x = self.rms_volume_norm(x, self.db_level)
         return x
 
-    def save_wav(self, wav: np.ndarray, path: str, sr: int = None, pipe_out=None) -> None:
+    def save_wav(self, wav: np.ndarray, path: str, sr: Optional[int] = None, pipe_out=None) -> None:
         """Save a waveform to a file using Scipy.
 
         Args:
@@ -599,18 +597,14 @@ class AudioProcessor:
             sr (int, optional): 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.
         """
-        if self.do_rms_norm:
-            wav_norm = self.rms_volume_norm(wav, self.db_level) * 32767
-        else:
-            wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
-
-        wav_norm = wav_norm.astype(np.int16)
-        if pipe_out:
-            wav_buffer = BytesIO()
-            scipy.io.wavfile.write(wav_buffer, sr if sr else self.sample_rate, wav_norm)
-            wav_buffer.seek(0)
-            pipe_out.buffer.write(wav_buffer.read())
-        scipy.io.wavfile.write(path, sr if sr else self.sample_rate, wav_norm)
+        save_wav(
+            wav=wav,
+            path=path,
+            sample_rate=sr if sr else self.sample_rate,
+            pipe_out=pipe_out,
+            do_rms_norm=self.do_rms_norm,
+            db_level=self.db_level,
+        )
 
     def get_duration(self, filename: str) -> float:
         """Get the duration of a wav file using Librosa.