feature: support .to(device) in tts and synthesizer

TTS/api.py

@@ -3,12 +3,14 @@
 import os
 import tempfile
 import urllib.request
+import warnings
 from pathlib import Path
 from typing import Tuple, Union
 
 import numpy as np
 import requests
 from scipy.io import wavfile
+from torch import nn
 
 from TTS.utils.audio.numpy_transforms import save_wav
 from TTS.utils.manage import ModelManager
@@ -229,7 +231,7 @@ def tts_to_file(
         return file_path
 
 
-class TTS:
+class TTS(nn.Module):
     """TODO: Add voice conversion and Capacitron support."""
 
     def __init__(
@@ -277,13 +279,17 @@ def __init__(
             progress_bar (bool, optional): Whether to pring a progress bar while downloading a model. Defaults to True.
             gpu (bool, optional): Enable/disable GPU. Some models might be too slow on CPU. Defaults to False.
         """
+        super().__init__()
         self.manager = ModelManager(models_file=self.get_models_file_path(), progress_bar=progress_bar, verbose=False)
 
         self.synthesizer = None
         self.voice_converter = None
         self.csapi = None
         self.model_name = None
 
+        if gpu:
+            warnings.warn("`gpu` will be deprecated. Please use `tts.to(device)` instead.")
+
         if model_name is not None:
             if "tts_models" in model_name or "coqui_studio" in model_name:
                 self.load_tts_model_by_name(model_name, gpu)

TTS/tts/utils/synthesis.py

@@ -5,19 +5,17 @@
 from torch import nn
 
 
-def numpy_to_torch(np_array, dtype, cuda=False):
+def numpy_to_torch(np_array, dtype, device="cpu"):
     if np_array is None:
         return None
-    tensor = torch.as_tensor(np_array, dtype=dtype)
-    if cuda:
-        return tensor.cuda()
+    tensor = torch.as_tensor(np_array, dtype=dtype, device=device)
     return tensor
 
 
-def compute_style_mel(style_wav, ap, cuda=False):
-    style_mel = torch.FloatTensor(ap.melspectrogram(ap.load_wav(style_wav, sr=ap.sample_rate))).unsqueeze(0)
-    if cuda:
-        return style_mel.cuda()
+def compute_style_mel(style_wav, ap, device="cpu"):
+    style_mel = torch.FloatTensor(
+        ap.melspectrogram(ap.load_wav(style_wav, sr=ap.sample_rate)), device=device,
+    ).unsqueeze(0)
     return style_mel
 
 
@@ -73,22 +71,18 @@ def inv_spectrogram(postnet_output, ap, CONFIG):
     return wav
 
 
-def id_to_torch(aux_id, cuda=False):
+def id_to_torch(aux_id, device="cpu"):
     if aux_id is not None:
         aux_id = np.asarray(aux_id)
-        aux_id = torch.from_numpy(aux_id)
-    if cuda:
-        return aux_id.cuda()
+        aux_id = torch.from_numpy(aux_id).to(device)
     return aux_id
 
 
-def embedding_to_torch(d_vector, cuda=False):
+def embedding_to_torch(d_vector, device="cpu"):
     if d_vector is not None:
         d_vector = np.asarray(d_vector)
         d_vector = torch.from_numpy(d_vector).type(torch.FloatTensor)
-        d_vector = d_vector.squeeze().unsqueeze(0)
-    if cuda:
-        return d_vector.cuda()
+        d_vector = d_vector.squeeze().unsqueeze(0).to(device)
     return d_vector
 
 
@@ -162,17 +156,20 @@ def synthesis(
         language_id (int):
             Language ID passed to the language embedding layer in multi-langual model. Defaults to None.
     """
+    # device
+    device = next(model.parameters()).device
+
     # GST or Capacitron processing
     # TODO: need to handle the case of setting both gst and capacitron to true somewhere
     style_mel = None
     if CONFIG.has("gst") and CONFIG.gst and style_wav is not None:
         if isinstance(style_wav, dict):
             style_mel = style_wav
         else:
-            style_mel = compute_style_mel(style_wav, model.ap, cuda=use_cuda)
+            style_mel = compute_style_mel(style_wav, model.ap, device=device)
 
     if CONFIG.has("capacitron_vae") and CONFIG.use_capacitron_vae and style_wav is not None:
-        style_mel = compute_style_mel(style_wav, model.ap, cuda=use_cuda)
+        style_mel = compute_style_mel(style_wav, model.ap, device=device)
         style_mel = style_mel.transpose(1, 2)  # [1, time, depth]
 
     language_name = None
@@ -188,26 +185,26 @@ def synthesis(
     )
     # pass tensors to backend
     if speaker_id is not None:
-        speaker_id = id_to_torch(speaker_id, cuda=use_cuda)
+        speaker_id = id_to_torch(speaker_id, device=device)
 
     if d_vector is not None:
-        d_vector = embedding_to_torch(d_vector, cuda=use_cuda)
+        d_vector = embedding_to_torch(d_vector, device=device)
 
     if language_id is not None:
-        language_id = id_to_torch(language_id, cuda=use_cuda)
+        language_id = id_to_torch(language_id, device=device)
 
     if not isinstance(style_mel, dict):
         # GST or Capacitron style mel
-        style_mel = numpy_to_torch(style_mel, torch.float, cuda=use_cuda)
+        style_mel = numpy_to_torch(style_mel, torch.float, device=device)
         if style_text is not None:
             style_text = np.asarray(
                 model.tokenizer.text_to_ids(style_text, language=language_id),
                 dtype=np.int32,
             )
-            style_text = numpy_to_torch(style_text, torch.long, cuda=use_cuda)
+            style_text = numpy_to_torch(style_text, torch.long, device=device)
             style_text = style_text.unsqueeze(0)
 
-    text_inputs = numpy_to_torch(text_inputs, torch.long, cuda=use_cuda)
+    text_inputs = numpy_to_torch(text_inputs, torch.long, device=device)
     text_inputs = text_inputs.unsqueeze(0)
     # synthesize voice
     outputs = run_model_torch(

TTS/utils/synthesizer.py

@@ -5,6 +5,7 @@
 import numpy as np
 import pysbd
 import torch
+from torch import nn
 
 from TTS.config import load_config
 from TTS.tts.configs.vits_config import VitsConfig
@@ -21,7 +22,7 @@
 from TTS.vocoder.utils.generic_utils import interpolate_vocoder_input
 
 
-class Synthesizer(object):
+class Synthesizer(nn.Module):
     def __init__(
         self,
         tts_checkpoint: str = "",
@@ -60,6 +61,7 @@ def __init__(
             vc_config (str, optional): path to the voice conversion config file. Defaults to `""`,
             use_cuda (bool, optional): enable/disable cuda. Defaults to False.
         """
+        super().__init__()
         self.tts_checkpoint = tts_checkpoint
         self.tts_config_path = tts_config_path
         self.tts_speakers_file = tts_speakers_file