Remove duplicate AudioProcessor code and fix ExtractTTSpectrogram.ipy…

…nb (#3230)

* chore: remove unused argument

* refactor(audio.processor): remove duplicate stft+griffin_lim

* chore(audio.processor): remove unused compute_stft_paddings

Same function available in numpy_transforms

* refactor(audio.processor): remove duplicate db_to_amp

* refactor(audio.processor): remove duplicate amp_to_db

* refactor(audio.processor): remove duplicate linear_to_mel

* refactor(audio.processor): remove duplicate mel_to_linear

* refactor(audio.processor): remove duplicate build_mel_basis

* refactor(audio.processor): remove duplicate stft_parameters

* refactor(audio.processor): use pre-/deemphasis from numpy_transforms

* refactor(audio.processor): use rms_volume_norm from numpy_transforms

* chore(audio.processor): remove duplicate assert

Already checked in numpy_transforms.compute_f0

* refactor(audio.processor): use find_endpoint from numpy_transforms

* refactor(audio.processor): use trim_silence from numpy_transforms

* refactor(audio.processor): use volume_norm from numpy_transforms

* refactor(audio.processor): use load_wav from numpy_transforms

* fix(bin.extract_tts_spectrograms): set quantization bits

* fix(ExtractTTSpectrogram.ipynb): adapt to current TTS code

Fixes #2447, #2574

* refactor(audio.processor): remove duplicate quantization methods

TTS/bin/extract_tts_spectrograms.py

@@ -15,6 +15,7 @@
 from TTS.tts.utils.speakers import SpeakerManager
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
+from TTS.utils.audio.numpy_transforms import quantize
 from TTS.utils.generic_utils import count_parameters
 
 use_cuda = torch.cuda.is_available()
@@ -159,7 +160,7 @@ def inference(
 
 
 def extract_spectrograms(
-    data_loader, model, ap, output_path, quantized_wav=False, save_audio=False, debug=False, metada_name="metada.txt"
+    data_loader, model, ap, output_path, quantize_bits=0, save_audio=False, debug=False, metada_name="metada.txt"
 ):
     model.eval()
     export_metadata = []
@@ -196,8 +197,8 @@ def extract_spectrograms(
             _, wavq_path, mel_path, wav_gl_path, wav_path = set_filename(wav_file_path, output_path)
 
             # quantize and save wav
-            if quantized_wav:
-                wavq = ap.quantize(wav)
+            if quantize_bits > 0:
+                wavq = quantize(wav, quantize_bits)
                 np.save(wavq_path, wavq)
 
             # save TTS mel
@@ -263,7 +264,7 @@ def main(args):  # pylint: disable=redefined-outer-name
         model,
         ap,
         args.output_path,
-        quantized_wav=args.quantized,
+        quantize_bits=args.quantize_bits,
         save_audio=args.save_audio,
         debug=args.debug,
         metada_name="metada.txt",
@@ -277,7 +278,7 @@ def main(args):  # pylint: disable=redefined-outer-name
     parser.add_argument("--output_path", type=str, help="Path to save mel specs", required=True)
     parser.add_argument("--debug", default=False, action="store_true", help="Save audio files for debug")
     parser.add_argument("--save_audio", default=False, action="store_true", help="Save audio files")
-    parser.add_argument("--quantized", action="store_true", help="Save quantized audio files")
+    parser.add_argument("--quantize_bits", type=int, default=0, help="Save quantized audio files if non-zero")
     parser.add_argument("--eval", type=bool, help="compute eval.", default=True)
     args = parser.parse_args()
 

TTS/utils/audio/numpy_transforms.py

@@ -201,7 +201,6 @@ def stft(
 def istft(
     *,
     y: np.ndarray = None,
-    fft_size: int = None,
     hop_length: int = None,
     win_length: int = None,
     window: str = "hann",