Allow running on CPU

tortoise/api.py

@@ -101,7 +101,7 @@ def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusi
                            conditioning_free=cond_free, conditioning_free_k=cond_free_k)
 
 
-def format_conditioning(clip, cond_length=132300):
+def format_conditioning(clip, cond_length=132300, device='cuda'):
     """
     Converts the given conditioning signal to a MEL spectrogram and clips it as expected by the models.
     """
@@ -112,7 +112,7 @@ def format_conditioning(clip, cond_length=132300):
         rand_start = random.randint(0, gap)
         clip = clip[:, rand_start:rand_start + cond_length]
     mel_clip = TorchMelSpectrogram()(clip.unsqueeze(0)).squeeze(0)
-    return mel_clip.unsqueeze(0).cuda()
+    return mel_clip.unsqueeze(0).to(device)
 
 
 def fix_autoregressive_output(codes, stop_token, complain=True):
@@ -181,14 +181,15 @@ def pick_best_batch_size_for_gpu():
     Tries to pick a batch size that will fit in your GPU. These sizes aren't guaranteed to work, but they should give
     you a good shot.
     """
-    free, available = torch.cuda.mem_get_info()
+    if torch.cuda.is_available():
-    availableGb = available / (1024 ** 3)
+        _, available = torch.cuda.mem_get_info()
-    if availableGb > 14:
+        availableGb = available / (1024 ** 3)
-        return 16
+        if availableGb > 14:
-    elif availableGb > 10:
+            return 16
-        return 8
+        elif availableGb > 10:
-    elif availableGb > 7:
+            return 8
-        return 4
+        elif availableGb > 7:
+            return 4
     return 1
 
 
@@ -197,7 +198,7 @@ class TextToSpeech:
     Main entry point into Tortoise.
     """
 
-    def __init__(self, autoregressive_batch_size=None, models_dir=MODELS_DIR, enable_redaction=True):
+    def __init__(self, autoregressive_batch_size=None, models_dir=MODELS_DIR, enable_redaction=True, device=None):
         """
         Constructor
         :param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing
@@ -207,10 +208,12 @@ class TextToSpeech:
         :param enable_redaction: When true, text enclosed in brackets are automatically redacted from the spoken output
                                  (but are still rendered by the model). This can be used for prompt engineering.
                                  Default is true.
+        :param device: Device to use when running the model. If omitted, the device will be automatically chosen.
         """
         self.models_dir = models_dir
         self.autoregressive_batch_size = pick_best_batch_size_for_gpu() if autoregressive_batch_size is None else autoregressive_batch_size
         self.enable_redaction = enable_redaction
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
         if self.enable_redaction:
             self.aligner = Wav2VecAlignment()
 
@@ -240,7 +243,7 @@ class TextToSpeech:
         self.cvvp = None # CVVP model is only loaded if used.
 
         self.vocoder = UnivNetGenerator().cpu()
-        self.vocoder.load_state_dict(torch.load(get_model_path('vocoder.pth', models_dir))['model_g'])
+        self.vocoder.load_state_dict(torch.load(get_model_path('vocoder.pth', models_dir), map_location=torch.device('cpu'))['model_g'])
         self.vocoder.eval(inference=True)
 
         # Random latent generators (RLGs) are loaded lazily.
@@ -261,15 +264,15 @@ class TextToSpeech:
         :param voice_samples: List of 2 or more ~10 second reference clips, which should be torch tensors containing 22.05kHz waveform data.
         """
         with torch.no_grad():
-            voice_samples = [v.to('cuda') for v in voice_samples]
+            voice_samples = [v.to(self.device) for v in voice_samples]
 
             auto_conds = []
             if not isinstance(voice_samples, list):
                 voice_samples = [voice_samples]
             for vs in voice_samples:
-                auto_conds.append(format_conditioning(vs))
+                auto_conds.append(format_conditioning(vs, self.device))
             auto_conds = torch.stack(auto_conds, dim=1)
-            self.autoregressive = self.autoregressive.cuda()
+            self.autoregressive = self.autoregressive.to(self.device)
             auto_latent = self.autoregressive.get_conditioning(auto_conds)
             self.autoregressive = self.autoregressive.cpu()
 
@@ -278,11 +281,11 @@ class TextToSpeech:
                 # The diffuser operates at a sample rate of 24000 (except for the latent inputs)
                 sample = torchaudio.functional.resample(sample, 22050, 24000)
                 sample = pad_or_truncate(sample, 102400)
-                cond_mel = wav_to_univnet_mel(sample.to('cuda'), do_normalization=False)
+                cond_mel = wav_to_univnet_mel(sample.to(self.device), do_normalization=False, device=self.device)
                 diffusion_conds.append(cond_mel)
             diffusion_conds = torch.stack(diffusion_conds, dim=1)
 
-            self.diffusion = self.diffusion.cuda()
+            self.diffusion = self.diffusion.to(self.device)
             diffusion_latent = self.diffusion.get_conditioning(diffusion_conds)
             self.diffusion = self.diffusion.cpu()
 
@@ -380,7 +383,7 @@ class TextToSpeech:
         """
         deterministic_seed = self.deterministic_state(seed=use_deterministic_seed)
 
-        text_tokens = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).cuda()
+        text_tokens = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).to(self.device)
         text_tokens = F.pad(text_tokens, (0, 1))  # This may not be necessary.
         assert text_tokens.shape[-1] < 400, 'Too much text provided. Break the text up into separate segments and re-try inference.'
 
@@ -391,8 +394,8 @@ class TextToSpeech:
             auto_conditioning, diffusion_conditioning = conditioning_latents
         else:
             auto_conditioning, diffusion_conditioning = self.get_random_conditioning_latents()
-        auto_conditioning = auto_conditioning.cuda()
+        auto_conditioning = auto_conditioning.to(self.device)
-        diffusion_conditioning = diffusion_conditioning.cuda()
+        diffusion_conditioning = diffusion_conditioning.to(self.device)
 
         diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k)
 
@@ -401,7 +404,7 @@ class TextToSpeech:
             num_batches = num_autoregressive_samples // self.autoregressive_batch_size
             stop_mel_token = self.autoregressive.stop_mel_token
             calm_token = 83  # This is the token for coding silence, which is fixed in place with "fix_autoregressive_output"
-            self.autoregressive = self.autoregressive.cuda()
+            self.autoregressive = self.autoregressive.to(self.device)
             if verbose:
                 print("Generating autoregressive samples..")
             for b in tqdm(range(num_batches), disable=not verbose):
@@ -420,11 +423,11 @@ class TextToSpeech:
             self.autoregressive = self.autoregressive.cpu()
 
             clip_results = []
-            self.clvp = self.clvp.cuda()
+            self.clvp = self.clvp.to(self.device)
             if cvvp_amount > 0:
                 if self.cvvp is None:
                     self.load_cvvp()
-                self.cvvp = self.cvvp.cuda()
+                self.cvvp = self.cvvp.to(self.device)
             if verbose:
                 if self.cvvp is None:
                     print("Computing best candidates using CLVP")
@@ -457,7 +460,7 @@ class TextToSpeech:
             # The diffusion model actually wants the last hidden layer from the autoregressive model as conditioning
             # inputs. Re-produce those for the top results. This could be made more efficient by storing all of these
             # results, but will increase memory usage.
-            self.autoregressive = self.autoregressive.cuda()
+            self.autoregressive = self.autoregressive.to(self.device)
             best_latents = self.autoregressive(auto_conditioning.repeat(k, 1), text_tokens.repeat(k, 1),
                                                torch.tensor([text_tokens.shape[-1]], device=text_tokens.device), best_results,
                                                torch.tensor([best_results.shape[-1]*self.autoregressive.mel_length_compression], device=text_tokens.device),
@@ -468,8 +471,8 @@ class TextToSpeech:
             if verbose:
                 print("Transforming autoregressive outputs into audio..")
             wav_candidates = []
-            self.diffusion = self.diffusion.cuda()
+            self.diffusion = self.diffusion.to(self.device)
-            self.vocoder = self.vocoder.cuda()
+            self.vocoder = self.vocoder.to(self.device)
             for b in range(best_results.shape[0]):
                 codes = best_results[b].unsqueeze(0)
                 latents = best_latents[b].unsqueeze(0)

tortoise/utils/audio.py

@@ -180,9 +180,9 @@ class TacotronSTFT(torch.nn.Module):
         return mel_output
 
 
-def wav_to_univnet_mel(wav, do_normalization=False):
+def wav_to_univnet_mel(wav, do_normalization=False, device='cuda'):
     stft = TacotronSTFT(1024, 256, 1024, 100, 24000, 0, 12000)
-    stft = stft.cuda()
+    stft = stft.to(device)
     mel = stft.mel_spectrogram(wav)
     if do_normalization:
         mel = normalize_tacotron_mel(mel)

tortoise/utils/wav2vec_alignment.py

@@ -49,17 +49,18 @@ class Wav2VecAlignment:
     """
     Uses wav2vec2 to perform audio<->text alignment.
     """
-    def __init__(self):
+    def __init__(self, device='cuda'):
         self.model = Wav2Vec2ForCTC.from_pretrained("jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli").cpu()
         self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"facebook/wav2vec2-large-960h")
         self.tokenizer = Wav2Vec2CTCTokenizer.from_pretrained('jbetker/tacotron-symbols')
+        self.device = device
 
     def align(self, audio, expected_text, audio_sample_rate=24000):
         orig_len = audio.shape[-1]
 
         with torch.no_grad():
-            self.model = self.model.cuda()
+            self.model = self.model.to(self.device)
-            audio = audio.to('cuda')
+            audio = audio.to(self.device)
             audio = torchaudio.functional.resample(audio, audio_sample_rate, 16000)
             clip_norm = (audio - audio.mean()) / torch.sqrt(audio.var() + 1e-7)
             logits = self.model(clip_norm).logits