Remove torchaudio requeriment

TTS/speaker_encoder/models/resnet.py

@@ -1,7 +1,10 @@
 import numpy as np
 import torch
-import torchaudio
+from torch import nn
-import torch.nn as nn
+
+# import torchaudio
+
+from TTS.utils.audio import TorchSTFT
 
 from TTS.utils.io import load_fsspec
 
@@ -110,14 +113,29 @@ class ResNetSpeakerEncoder(nn.Module):
         if self.use_torch_spec:
             self.torch_spec = torch.nn.Sequential(
                 PreEmphasis(audio_config["preemphasis"]),
-                torchaudio.transforms.MelSpectrogram(
+                TorchSTFT(
+                    n_fft=audio_config["fft_size"],
+                    hop_length=audio_config["hop_length"],
+                    win_length=audio_config["win_length"],
+                    sample_rate=audio_config["sample_rate"],
+                    window="hamming_window",
+                    mel_fmin=0.0,
+                    mel_fmax=None,
+                    use_htk=True,
+                    do_amp_to_db=False,
+                    n_mels=audio_config["num_mels"],
+                    power=2.0,
+                    use_mel=True,
+                    mel_norm=None
+                ),
+                '''torchaudio.transforms.MelSpectrogram(
                     sample_rate=audio_config["sample_rate"],
                     n_fft=audio_config["fft_size"],
                     win_length=audio_config["win_length"],
                     hop_length=audio_config["hop_length"],
                     window_fn=torch.hamming_window,
                     n_mels=audio_config["num_mels"],
-                ),
+                ),'''
             )
         else:
             self.torch_spec = None

TTS/tts/models/vits.py

@@ -4,7 +4,7 @@ from itertools import chain
 from typing import Dict, List, Tuple
 
 import torch
-import torchaudio
+# import torchaudio
 from coqpit import Coqpit
 from torch import nn
 from torch.cuda.amp.autocast_mode import autocast
@@ -395,7 +395,7 @@ class Vits(BaseTTS):
         if config.use_speaker_encoder_as_loss:
             if not config.speaker_encoder_model_path or not config.speaker_encoder_config_path:
                 raise RuntimeError(
-                    " [!] To use the speaker encoder loss you need to specify speaker_encoder_model_path and speaker_encoder_config_path !!"
+                    " [!] To use the speaker consistency loss (SCL) you need to specify speaker_encoder_model_path and speaker_encoder_config_path !!"
                 )
             self.speaker_manager.init_speaker_encoder(
                 config.speaker_encoder_model_path, config.speaker_encoder_config_path
@@ -410,14 +410,17 @@ class Vits(BaseTTS):
                 hasattr(self.speaker_encoder, "audio_config")
                 and self.audio_config["sample_rate"] != self.speaker_encoder.audio_config["sample_rate"]
             ):
-                self.audio_transform = torchaudio.transforms.Resample(
+                raise RuntimeError(
+                    " [!] To use the speaker consistency loss (SCL) you need to have the TTS model sampling rate ({})  equal to the speaker encoder sampling rate ({}) !".format(self.audio_config["sample_rate"], self.speaker_encoder.audio_config["sample_rate"])
+                )
+                '''self.audio_transform = torchaudio.transforms.Resample(
                     orig_freq=self.audio_config["sample_rate"],
                     new_freq=self.speaker_encoder.audio_config["sample_rate"],
                     )
                 else:
-                self.audio_transform = None
+                    self.audio_transform = None'''
         else:
-            self.audio_transform = None
+            # self.audio_transform = None
             self.speaker_encoder = None
 
     def _init_speaker_embedding(self, config):
@@ -655,8 +658,8 @@ class Vits(BaseTTS):
             wavs_batch = torch.cat((wav_seg, o), dim=0).squeeze(1)
 
             # resample audio to speaker encoder sample_rate
-            if self.audio_transform is not None:
+            '''if self.audio_transform is not None:
-                wavs_batch = self.audio_transform(wavs_batch)
+                wavs_batch = self.audio_transform(wavs_batch)'''
 
             pred_embs = self.speaker_encoder.forward(wavs_batch, l2_norm=True)
 

TTS/utils/audio.py

@@ -32,6 +32,9 @@ class TorchSTFT(nn.Module):  # pylint: disable=abstract-method
         use_mel=False,
         do_amp_to_db=False,
         spec_gain=1.0,
+        power=None,
+        use_htk=False,
+        mel_norm="slaney"
     ):
         super().__init__()
         self.n_fft = n_fft
@@ -45,6 +48,9 @@ class TorchSTFT(nn.Module):  # pylint: disable=abstract-method
         self.use_mel = use_mel
         self.do_amp_to_db = do_amp_to_db
         self.spec_gain = spec_gain
+        self.power = power
+        self.use_htk = use_htk
+        self.mel_norm = mel_norm
         self.window = nn.Parameter(getattr(torch, window)(win_length), requires_grad=False)
         self.mel_basis = None
         if use_mel:
@@ -83,6 +89,10 @@ class TorchSTFT(nn.Module):  # pylint: disable=abstract-method
         M = o[:, :, :, 0]
         P = o[:, :, :, 1]
         S = torch.sqrt(torch.clamp(M ** 2 + P ** 2, min=1e-8))
+
+        if self.power is not None:
+            S = S ** self.power
+
         if self.use_mel:
             S = torch.matmul(self.mel_basis.to(x), S)
         if self.do_amp_to_db:
@@ -91,7 +101,7 @@ class TorchSTFT(nn.Module):  # pylint: disable=abstract-method
 
     def _build_mel_basis(self):
         mel_basis = librosa.filters.mel(
-            self.sample_rate, self.n_fft, n_mels=self.n_mels, fmin=self.mel_fmin, fmax=self.mel_fmax
+            self.sample_rate, self.n_fft, n_mels=self.n_mels, fmin=self.mel_fmin, fmax=self.mel_fmax, htk=self.use_htk, norm=self.mel_norm
         )
         self.mel_basis = torch.from_numpy(mel_basis).float()
 

requirements.txt

@@ -26,5 +26,3 @@ unidic-lite==1.0.8
 gruut[cs,de,es,fr,it,nl,pt,ru,sv]~=2.0.0
 fsspec>=2021.04.0
 pyworld
-webrtcvad
-torchaudio>=0.7