misc fixes

tortoise/api.py

@@ -37,6 +37,8 @@ def download_models(specific_models=None):
         'cvvp.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/cvvp.pth',
         'diffusion_decoder.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/diffusion_decoder.pth',
         'vocoder.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/vocoder.pth',
+        'rlg_auto.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/rlg_auto.pth',
+        'rlg_diffuser.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/rlg_diffuser.pth',
     }
     os.makedirs('.models', exist_ok=True)
     def show_progress(block_num, block_size, total_size):
@@ -110,9 +112,9 @@ def fix_autoregressive_output(codes, stop_token, complain=True):
     stop_token_indices = (codes == stop_token).nonzero()
     if len(stop_token_indices) == 0:
         if complain:
-            print("No stop tokens found. This typically means the spoken audio is too long. In some cases, the output "
+            print("No stop tokens found in one of the generated voice clips. This typically means the spoken audio is "
-                  "will still be good, though. Listen to it and if it is missing words, try breaking up your input "
+                  "too long. In some cases, the output will still be good, though. Listen to it and if it is missing words, "
-                  "text.")
+                  "try breaking up your input text.")
         return codes
     else:
         codes[stop_token_indices] = 83
@@ -163,8 +165,7 @@ class TextToSpeech:
     Main entry point into Tortoise.
     """
 
-    def __init__(self, autoregressive_batch_size=16, models_dir='.models', enable_redaction=True,
+    def __init__(self, autoregressive_batch_size=16, models_dir='.models', enable_redaction=True):
-                 save_random_voices=False):
         """
         Constructor
         :param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing
@@ -174,14 +175,11 @@ 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 save_random_voices: When true, voices that are randomly generated are saved to the `random_voices`
-                                   directory. Default is false.
         """
         self.autoregressive_batch_size = autoregressive_batch_size
         self.enable_redaction = enable_redaction
         if self.enable_redaction:
             self.aligner = Wav2VecAlignment()
-        self.save_random_voices = save_random_voices
 
         self.tokenizer = VoiceBpeTokenizer()
         download_models()
@@ -220,29 +218,6 @@ class TextToSpeech:
         self.rlg_auto = None
         self.rlg_diffusion = None
 
-    def tts_with_preset(self, text, preset='fast', **kwargs):
-        """
-        Calls TTS with one of a set of preset generation parameters. Options:
-            'ultra_fast': Produces speech at a speed which belies the name of this repo. (Not really, but it's definitely fastest).
-            'fast': Decent quality speech at a decent inference rate. A good choice for mass inference.
-            'standard': Very good quality. This is generally about as good as you are going to get.
-            'high_quality': Use if you want the absolute best. This is not really worth the compute, though.
-        """
-        # Use generally found best tuning knobs for generation.
-        kwargs.update({'temperature': .8, 'length_penalty': 1.0, 'repetition_penalty': 2.0,
-                       #'typical_sampling': True,
-                       'top_p': .8,
-                       'cond_free_k': 2.0, 'diffusion_temperature': 1.0})
-        # Presets are defined here.
-        presets = {
-            'ultra_fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 32, 'cond_free': False},
-            'fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 32},
-            'standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 128},
-            'high_quality': {'num_autoregressive_samples': 512, 'diffusion_iterations': 1024},
-        }
-        kwargs.update(presets[preset])
-        return self.tts(text, **kwargs)
-
     def get_conditioning_latents(self, voice_samples, return_mels=False):
         """
         Transforms one or more voice_samples into a tuple (autoregressive_conditioning_latent, diffusion_conditioning_latent).
@@ -288,11 +263,30 @@ class TextToSpeech:
             self.rlg_diffusion = RandomLatentConverter(2048).eval()
             self.rlg_diffusion.load_state_dict(torch.load('.models/rlg_diffuser.pth', map_location=torch.device('cpu')))
         with torch.no_grad():
-            latents = self.rlg_auto(torch.tensor([0.0])), self.rlg_diffusion(torch.tensor([0.0]))
+            return self.rlg_auto(torch.tensor([0.0])), self.rlg_diffusion(torch.tensor([0.0]))
-            if self.save_random_voices:
+
-                os.makedirs('random_voices', exist_ok=True)
+    def tts_with_preset(self, text, preset='fast', **kwargs):
-                torch.save(latents, f'random_voices/{str(uuid.uuid4())}.pth')
+        """
-            return latents
+        Calls TTS with one of a set of preset generation parameters. Options:
+            'ultra_fast': Produces speech at a speed which belies the name of this repo. (Not really, but it's definitely fastest).
+            'fast': Decent quality speech at a decent inference rate. A good choice for mass inference.
+            'standard': Very good quality. This is generally about as good as you are going to get.
+            'high_quality': Use if you want the absolute best. This is not really worth the compute, though.
+        """
+        # Use generally found best tuning knobs for generation.
+        kwargs.update({'temperature': .8, 'length_penalty': 1.0, 'repetition_penalty': 2.0,
+                       #'typical_sampling': True,
+                       'top_p': .8,
+                       'cond_free_k': 2.0, 'diffusion_temperature': 1.0})
+        # Presets are defined here.
+        presets = {
+            'ultra_fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 32, 'cond_free': False},
+            'fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 32},
+            'standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 128},
+            'high_quality': {'num_autoregressive_samples': 512, 'diffusion_iterations': 1024},
+        }
+        kwargs.update(presets[preset])
+        return self.tts(text, **kwargs)
 
     def tts(self, text, voice_samples=None, conditioning_latents=None, k=1, verbose=True,
             # autoregressive generation parameters follow
@@ -452,7 +446,7 @@ class TextToSpeech:
 
             def potentially_redact(clip, text):
                 if self.enable_redaction:
-                    return self.aligner.redact(clip, text)
+                    return self.aligner.redact(clip.squeeze(1), text).unsqueeze(1)
                 return clip
             wav_candidates = [potentially_redact(wav_candidate, text) for wav_candidate in wav_candidates]
             if len(wav_candidates) > 1:

tortoise/do_tts.py

@@ -8,7 +8,7 @@ from tortoise.utils.audio import load_audio, get_voices, load_voice
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('--text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.")
+    parser.add_argument('--text', type=str, help='Text to speak.', default="The expressiveness of autoregressive transformers is literally nuts! I absolutely adore them.")
     parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
                                                  'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='random')
     parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='fast')
@@ -21,7 +21,7 @@ if __name__ == '__main__':
     args = parser.parse_args()
     os.makedirs(args.output_path, exist_ok=True)
 
-    tts = TextToSpeech(models_dir=args.model_dir, save_random_voices=True)
+    tts = TextToSpeech(models_dir=args.model_dir)
 
     selected_voices = args.voice.split(',')
     for k, voice in enumerate(selected_voices):

tortoise/is_this_from_tortoise.py

@@ -5,7 +5,7 @@ from tortoise.utils.audio import load_audio
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('--clip', type=str, help='Path to an audio clip to classify.', default="results/favorite_riding_hood.mp3")
+    parser.add_argument('--clip', type=str, help='Path to an audio clip to classify.', default="../examples/favorite_riding_hood.mp3")
     args = parser.parse_args()
 
     clip = load_audio(args.clip, 24000)

tortoise/read.py

@@ -40,7 +40,7 @@ if __name__ == '__main__':
     parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
                                                       'should only be specified if you have custom checkpoints.', default='.models')
     args = parser.parse_args()
-    tts = TextToSpeech(models_dir=args.model_dir, save_random_voices=True)
+    tts = TextToSpeech(models_dir=args.model_dir)
 
     outpath = args.output_path
     selected_voices = args.voice.split(',')

tortoise/utils/audio.py

@@ -114,7 +114,7 @@ def load_voices(voices):
         if voice == 'random':
             print("Cannot combine a random voice with a non-random voice. Just using a random voice.")
             return None, None
-        latent, clip = load_voice(voice)
+        clip, latent = load_voice(voice)
         if latent is None:
             assert len(latents) == 0, "Can only combine raw audio voices or latent voices, not both. Do it yourself if you want this."
             clips.extend(clip)

tortoise/utils/wav2vec_alignment.py

@@ -1,3 +1,5 @@
+import re
+
 import torch
 import torchaudio
 from transformers import Wav2Vec2ForCTC, Wav2Vec2FeatureExtractor, Wav2Vec2CTCTokenizer, Wav2Vec2Processor
@@ -11,7 +13,7 @@ class Wav2VecAlignment:
         self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"facebook/wav2vec2-large-960h")
         self.tokenizer = Wav2Vec2CTCTokenizer.from_pretrained('jbetker/tacotron_symbols')
 
-    def align(self, audio, expected_text, audio_sample_rate=24000, topk=3):
+    def align(self, audio, expected_text, audio_sample_rate=24000, topk=3, return_partial=False):
         orig_len = audio.shape[-1]
 
         with torch.no_grad():
@@ -41,7 +43,9 @@ class Wav2VecAlignment:
 
         if len(expected_tokens) > 0:
             print(f"Alignment did not work. {len(expected_tokens)} were not found, with the following string un-aligned:"
-                  f" {self.tokenizer.decode(expected_tokens)}")
+                  f" `{self.tokenizer.decode(expected_tokens)}`. Here's what wav2vec thought it heard:"
+                  f"`{self.tokenizer.decode(logits.argmax(-1).tolist())}`")
+            if not return_partial:
                 return None
 
         return alignments
@@ -54,6 +58,8 @@ class Wav2VecAlignment:
         for spl in splitted[1:]:
             assert ']' in spl, 'Every "[" character must be paired with a "]" with no nesting.'
             fully_split.extend(spl.split(']'))
+        # Remove any non-alphabetic character in the input text. This makes matching more likely.
+        fully_split = [re.sub(r'[^a-zA-Z ]', '', s) for s in fully_split]
         # At this point, fully_split is a list of strings, with every other string being something that should be redacted.
         non_redacted_intervals = []
         last_point = 0
@@ -63,20 +69,22 @@ class Wav2VecAlignment:
             last_point += len(fully_split[i])
 
         bare_text = ''.join(fully_split)
-        alignments = self.align(audio, bare_text, audio_sample_rate, topk)
+        alignments = self.align(audio, bare_text, audio_sample_rate, topk, return_partial=True)
-        if alignments is None:
+        # If alignment fails, we will attempt to recover by assuming the remaining alignments consume the rest of the string.
-            return audio  # Cannot redact because alignment did not succeed.
+        def get_alignment(i):
+            if i >= len(alignments):
+                return audio.shape[-1]
 
         output_audio = []
         for nri in non_redacted_intervals:
             start, stop = nri
-            output_audio.append(audio[:, alignments[start]:alignments[stop]])
+            output_audio.append(audio[:, get_alignment(start):get_alignment(stop)])
         return torch.cat(output_audio, dim=-1)
 
 
 if __name__ == '__main__':
-    some_audio = load_audio('../../results/favorites/morgan_freeman_metallic_hydrogen.mp3', 24000)
+    some_audio = load_audio('../../results/train_dotrice_0.wav', 24000)
     aligner = Wav2VecAlignment()
-    text = "instead of molten iron, jupiter [and brown dwaves] have hydrogen, which [is under so much pressure that it] develops metallic properties"
+    text = "[God fucking damn it I'm so angry] The expressiveness of autoregressive transformers is literally nuts! I absolutely adore them."
     redact = aligner.redact(some_audio, text)
     torchaudio.save(f'test_output.wav', redact, 24000)