Add device support in TTS and Synthesizer

TTS/api.py

@@ -1,16 +1,18 @@
 import tempfile
+import warnings
 from pathlib import Path
 from typing import Union
 
 import numpy as np
+from torch import nn
 
 from TTS.cs_api import CS_API
 from TTS.utils.audio.numpy_transforms import save_wav
 from TTS.utils.manage import ModelManager
 from TTS.utils.synthesizer import Synthesizer
 
 
-class TTS:
+class TTS(nn.Module):
     """TODO: Add voice conversion and Capacitron support."""
 
     def __init__(
@@ -62,6 +64,7 @@ def __init__(
                 Defaults to "XTTS".
             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
@@ -70,6 +73,9 @@ def __init__(
         self.cs_api_model = cs_api_model
         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 +5,21 @@
 from torch import nn
 
 
-def numpy_to_torch(np_array, dtype, cuda=False):
+def numpy_to_torch(np_array, dtype, cuda=False, device="cpu"):
+    if cuda:
+        device = "cuda"
     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)
+def compute_style_mel(style_wav, ap, cuda=False, device="cpu"):
     if cuda:
-        return style_mel.cuda()
+        device = "cuda"
+    style_mel = torch.FloatTensor(
+        ap.melspectrogram(ap.load_wav(style_wav, sr=ap.sample_rate)), device=device,
+    ).unsqueeze(0)
     return style_mel
 
 
@@ -73,22 +75,22 @@ def inv_spectrogram(postnet_output, ap, CONFIG):
     return wav
 
 
-def id_to_torch(aux_id, cuda=False):
+def id_to_torch(aux_id, cuda=False, device="cpu"):
+    if cuda:
+        device = "cuda"
     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, cuda=False, device="cpu"):
+    if cuda:
+        device = "cuda"
     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 +164,22 @@ 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
+    if use_cuda:
+        device = "cuda"
+
     # 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 +195,26 @@ 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(
@@ -290,22 +297,27 @@ def transfer_voice(
         do_trim_silence (bool):
             trim silence after synthesis. Defaults to False.
     """
+    # device
+    device = next(model.parameters()).device
+    if use_cuda:
+        device = "cuda"
+
     # 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 reference_d_vector is not None:
-        reference_d_vector = embedding_to_torch(reference_d_vector, cuda=use_cuda)
+        reference_d_vector = embedding_to_torch(reference_d_vector, device=device)
 
     # load reference_wav audio
     reference_wav = embedding_to_torch(
         model.ap.load_wav(
             reference_wav, sr=model.args.encoder_sample_rate if model.args.encoder_sample_rate else model.ap.sample_rate
         ),
-        cuda=use_cuda,
+        device=device,
     )
 
     if hasattr(model, "module"):

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
@@ -356,7 +358,12 @@ def tts(
         if speaker_wav is not None and self.tts_model.speaker_manager is not None:
             speaker_embedding = self.tts_model.speaker_manager.compute_embedding_from_clip(speaker_wav)
 
+        vocoder_device = "cpu"
         use_gl = self.vocoder_model is None
+        if not use_gl:
+            vocoder_device = next(self.vocoder_model.parameters()).device
+        if self.use_cuda:
+            vocoder_device = "cuda"
 
         if not reference_wav:  # not voice conversion
             for sen in sens:
@@ -388,7 +395,6 @@ def tts(
                     mel_postnet_spec = outputs["outputs"]["model_outputs"][0].detach().cpu().numpy()
                     # denormalize tts output based on tts audio config
                     mel_postnet_spec = self.tts_model.ap.denormalize(mel_postnet_spec.T).T
-                    device_type = "cuda" if self.use_cuda else "cpu"
                     # renormalize spectrogram based on vocoder config
                     vocoder_input = self.vocoder_ap.normalize(mel_postnet_spec.T)
                     # compute scale factor for possible sample rate mismatch
@@ -403,8 +409,8 @@ def tts(
                         vocoder_input = torch.tensor(vocoder_input).unsqueeze(0)  # pylint: disable=not-callable
                     # run vocoder model
                     # [1, T, C]
-                    waveform = self.vocoder_model.inference(vocoder_input.to(device_type))
-                if self.use_cuda and not use_gl:
+                    waveform = self.vocoder_model.inference(vocoder_input.to(vocoder_device))
+                if torch.is_tensor(waveform) and waveform.device != torch.device("cpu") and not use_gl:
                     waveform = waveform.cpu()
                 if not use_gl:
                     waveform = waveform.numpy()
@@ -453,7 +459,6 @@ def tts(
                 mel_postnet_spec = outputs[0].detach().cpu().numpy()
                 # denormalize tts output based on tts audio config
                 mel_postnet_spec = self.tts_model.ap.denormalize(mel_postnet_spec.T).T
-                device_type = "cuda" if self.use_cuda else "cpu"
                 # renormalize spectrogram based on vocoder config
                 vocoder_input = self.vocoder_ap.normalize(mel_postnet_spec.T)
                 # compute scale factor for possible sample rate mismatch
@@ -468,8 +473,8 @@ def tts(
                     vocoder_input = torch.tensor(vocoder_input).unsqueeze(0)  # pylint: disable=not-callable
                 # run vocoder model
                 # [1, T, C]
-                waveform = self.vocoder_model.inference(vocoder_input.to(device_type))
-            if self.use_cuda:
+                waveform = self.vocoder_model.inference(vocoder_input.to(vocoder_device))
+            if torch.is_tensor(waveform) and waveform.device != torch.device("cpu"):
                 waveform = waveform.cpu()
             if not use_gl:
                 waveform = waveform.numpy()