add ability to pass tortoise presets through coqui api

TTS/api.py

@@ -504,6 +504,7 @@ class TTS:
         speaker_wav: str = None,
         emotion: str = None,
         speed: float = None,
+        **kwargs,
     ):
         """Convert text to speech.
 
@@ -540,6 +541,7 @@ class TTS:
             style_wav=None,
             style_text=None,
             reference_speaker_name=None,
+            **kwargs,
         )
         return wav
 
@@ -552,6 +554,7 @@ class TTS:
         emotion: str = "Neutral",
         speed: float = 1.0,
         file_path: str = "output.wav",
+        **kwargs,
     ):
         """Convert text to speech.
 
@@ -580,7 +583,7 @@ class TTS:
             return self.tts_coqui_studio(
                 text=text, speaker_name=speaker, language=language, emotion=emotion, speed=speed, file_path=file_path
             )
-        wav = self.tts(text=text, speaker=speaker, language=language, speaker_wav=speaker_wav)
+        wav = self.tts(text=text, speaker=speaker, language=language, speaker_wav=speaker_wav, **kwargs)
         self.synthesizer.save_wav(wav=wav, path=file_path)
         return file_path
 

TTS/tts/layers/tortoise/arch_utils.py

@@ -1,8 +1,8 @@
 import functools
 import math
 import os
-import fsspec
 
+import fsspec
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

TTS/tts/layers/tortoise/tokenizer.py

@@ -2,8 +2,8 @@ import os
 
 import torch
 from tokenizers import Tokenizer
-from TTS.tts.utils.text.cleaners import english_cleaners
 
+from TTS.tts.utils.text.cleaners import english_cleaners
 
 DEFAULT_VOCAB_FILE = os.path.join(
     os.path.dirname(os.path.realpath(__file__)), "../../utils/assets/tortoise/tokenizer.json"

TTS/tts/layers/tortoise/utils.py

@@ -5,20 +5,17 @@ from tqdm import tqdm
 
 DEFAULT_MODELS_DIR = os.path.join(os.path.expanduser("~"), ".cache", "tortoise", "models")
 MODELS_DIR = os.environ.get("TORTOISE_MODELS_DIR", DEFAULT_MODELS_DIR)
-
+MODELS_DIR = "/data/speech_synth/models/"
 MODELS = {
     "autoregressive.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/autoregressive.pth",
     "classifier.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/classifier.pth",
     "clvp2.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/clvp2.pth",
-    "cvvp.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/cvvp.pth",
     "diffusion_decoder.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/diffusion_decoder.pth",
     "vocoder.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/vocoder.pth",
     "rlg_auto.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_auto.pth",
     "rlg_diffuser.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_diffuser.pth",
 }
 
-pbar = None
-
 
 def download_models(specific_models=None):
     """

TTS/tts/models/tortoise.py

@@ -11,13 +11,7 @@ from coqpit import Coqpit
 from tqdm import tqdm
 
 from TTS.tts.layers.tortoise.arch_utils import TorchMelSpectrogram
-from TTS.tts.layers.tortoise.audio_utils import (
+from TTS.tts.layers.tortoise.audio_utils import denormalize_tacotron_mel, load_voice, wav_to_univnet_mel
-    denormalize_tacotron_mel,
-    load_audio,
-    load_voice,
-    load_voices,
-    wav_to_univnet_mel,
-)
 from TTS.tts.layers.tortoise.autoregressive import UnifiedVoice
 from TTS.tts.layers.tortoise.classifier import AudioMiniEncoderWithClassifierHead
 from TTS.tts.layers.tortoise.clvp import CLVP
@@ -25,7 +19,7 @@ from TTS.tts.layers.tortoise.diffusion import SpacedDiffusion, get_named_beta_sc
 from TTS.tts.layers.tortoise.diffusion_decoder import DiffusionTts
 from TTS.tts.layers.tortoise.random_latent_generator import RandomLatentConverter
 from TTS.tts.layers.tortoise.tokenizer import VoiceBpeTokenizer
-from TTS.tts.layers.tortoise.utils import MODELS_DIR, get_model_path
+from TTS.tts.layers.tortoise.utils import get_model_path
 from TTS.tts.layers.tortoise.vocoder import VocConf
 from TTS.tts.layers.tortoise.wav2vec_alignment import Wav2VecAlignment
 from TTS.tts.models.base_tts import BaseTTS
@@ -498,16 +492,11 @@ class Tortoise(BaseTTS):
         with torch.no_grad():
             return self.rlg_auto(torch.tensor([0.0])), self.rlg_diffusion(torch.tensor([0.0]))
 
-    def synthesis(
+    def synthesis(self, text, config, speaker_id="lj", **kwargs):
-        self,
-        text,
-        config,
-        speaker_id="lj",
-    ):
         voice_samples, conditioning_latents = load_voice(speaker_id)
 
         outputs = self.inference_with_config(
-            text, config, voice_samples=voice_samples, conditioning_latents=conditioning_latents
+            text, config, voice_samples=voice_samples, conditioning_latents=conditioning_latents, **kwargs
         )
 
         return_dict = {
@@ -533,10 +522,47 @@ class Tortoise(BaseTTS):
             "top_p": config.top_p,
             "cond_free_k": config.cond_free_k,
             "diffusion_temperature": config.diffusion_temperature,
-            "num_autoregressive_samples": config.num_autoregressive_samples,
-            "diffusion_iterations": config.diffusion_iterations,
             "sampler": config.sampler,
         }
+        # Presets are defined here.
+        presets = {
+            "single_sample": {
+                "num_autoregressive_samples": 8,
+                "diffusion_iterations": 10,
+                "sampler": "ddim",
+            },
+            "ultra_fast": {
+                "num_autoregressive_samples": 16,
+                "diffusion_iterations": 10,
+                "sampler": "ddim",
+            },
+            "ultra_fast_old": {
+                "num_autoregressive_samples": 16,
+                "diffusion_iterations": 30,
+                "cond_free": False,
+            },
+            "very_fast": {
+                "num_autoregressive_samples": 32,
+                "diffusion_iterations": 30,
+                "sampler": "dpm++2m",
+            },
+            "fast": {
+                "num_autoregressive_samples": 5,
+                "diffusion_iterations": 50,
+                "sampler": "ddim",
+            },
+            "fast_old": {"num_autoregressive_samples": 96, "diffusion_iterations": 80},
+            "standard": {
+                "num_autoregressive_samples": 5,
+                "diffusion_iterations": 200,
+            },
+            "high_quality": {
+                "num_autoregressive_samples": 256,
+                "diffusion_iterations": 400,
+            },
+        }
+        settings.update(presets[kwargs["preset"]])
+        kwargs.pop("preset")
         settings.update(kwargs)  # allow overriding of preset settings with kwargs
         return self.inference(text, **settings)
 
@@ -551,7 +577,7 @@ class Tortoise(BaseTTS):
         return_deterministic_state=False,
         latent_averaging_mode=0,
         # autoregressive generation parameters follow
-        num_autoregressive_samples=512,
+        num_autoregressive_samples=16,
         temperature=0.8,
         length_penalty=1,
         repetition_penalty=2.0,

TTS/utils/synthesizer.py

@@ -228,6 +228,7 @@ class Synthesizer(object):
         style_text=None,
         reference_wav=None,
         reference_speaker_name=None,
+        **kwargs,
     ) -> List[int]:
         """🐸 TTS magic. Run all the models and generate speech.
 
@@ -328,7 +329,7 @@ class Synthesizer(object):
         if not reference_wav:
             for sen in sens:
                 if self.tts_config.model == "tortoise":
-                    outputs = self.tts_model.synthesis(text=sen, config=self.tts_config)
+                    outputs = self.tts_model.synthesis(text=sen, config=self.tts_config, **kwargs)
                 else:
                     # synthesize voice
                     outputs = synthesis(