Implemented the tuning parameters into the CLI

README.md

@@ -87,6 +87,11 @@ This script allows you to speak a single phrase with one or more voices.
 python tortoise/do_tts.py --text "I'm going to speak this" --voice random --preset fast
 ```
 
+List available parameters
+```shell
+python tortoise/do_tts.py --help
+```
+
 ### read.py
 
 This script provides tools for reading large amounts of text.

tortoise/api.py

@@ -329,6 +329,9 @@ def tts_with_preset(self, text, preset='fast', **kwargs):
             'standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200},
             'high_quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400},
         }
+        # Filter out 'None' parameters.
+        kwargs = {k: v for k, v in kwargs.items() if v is not None}
+
         settings.update(presets[preset])
         settings.update(kwargs) # allow overriding of preset settings with kwargs
         return self.tts(text, **settings)

tortoise/do_tts.py

@@ -9,19 +9,28 @@
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    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')
-    parser.add_argument('--use_deepspeed', type=str, help='Which voice preset to use.', default=False)
-    parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/')
-    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_DIR)
-    parser.add_argument('--candidates', type=int, help='How many output candidates to produce per-voice.', default=3)
-    parser.add_argument('--seed', type=int, help='Random seed which can be used to reproduce results.', default=None)
-    parser.add_argument('--produce_debug_state', type=bool, help='Whether or not to produce debug_state.pth, which can aid in reproducing problems. Defaults to true.', default=True)
-    parser.add_argument('--cvvp_amount', type=float, help='How much the CVVP model should influence the output.'
-                                                          'Increasing this can in some cases reduce the likelihood of multiple speakers. Defaults to 0 (disabled)', default=.0)
+    parser.add_argument('--text', type=str, default="The expressiveness of autoregressive transformers is literally nuts! I absolutely adore them.", help='Text to speak.')
+    parser.add_argument('--voice', type=str, default='random', 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.')
+    parser.add_argument('--preset', type=str, default='fast', help='Which voice preset to use.')
+    parser.add_argument('--use_deepspeed', type=str, default=False, help='Which voice preset to use.')
+    parser.add_argument('--output_path', type=str, default='results/', help='Where to store outputs.')
+    parser.add_argument('--model_dir', type=str, default=MODELS_DIR, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this should only be specified if you have custom checkpoints.')
+    parser.add_argument('--candidates', type=int, default=3, help='How many output candidates to produce per-voice.')
+    parser.add_argument('--seed', type=int, default=None, help='Random seed which can be used to reproduce results.')
+    parser.add_argument('--produce_debug_state', type=bool, default=True, help='Whether or not to produce debug_state.pth, which can aid in reproducing problems. Defaults to true.')
+    parser.add_argument('--cvvp_amount', type=float, default=.0, help='How much the CVVP model should influence the output. Increasing this can in some cases reduce the likelihood of multiple speakers. Defaults to 0 (disabled)')
+    parser.add_argument('--models-dir', type=str, default=MODELS_DIR, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to ~/.cache/tortoise/.models, so this should only be specified if you have custom checkpoints.')
+    parser.add_argument('--num_autoregressive_samples', type=int, default=None, help='Number of samples taken from the autoregressive model, all of which are filtered using CLVP. As TorToiSe is a probabilistic model, more samples means a higher probability of creating something "great".')
+    parser.add_argument('--temperature', type=float, default=None, help='The softmax temperature of the autoregressive model.')
+    parser.add_argument('--length_penalty', type=float, default=None, help='A length penalty applied to the autoregressive decoder. Higher settings causes the model to produce more terse outputs.')
+    parser.add_argument('--repetition_penalty', type=float, default=None, help='A penalty that prevents the autoregressive decoder from repeating itself during decoding. Can be used to reduce the incidence of long silences or "uhhhhhhs", etc.')
+    parser.add_argument('--top_p', type=float, default=None, help='P value used in nucleus sampling. 0 to 1. Lower values mean the decoder produces more "likely" (aka boring) outputs.')
+    parser.add_argument('--max_mel_tokens', type=int, default=None, help='Restricts the output length. 1 to 600. Each unit is 1/20 of a second.')
+    parser.add_argument('--diffusion_iterations', type=int, default=None, help='Number of diffusion steps to perform.  More steps means the network has more chances to iteratively refine the output, which should theoretically mean a higher quality output. Generally a value above 250 is not noticeably better, however.')
+    parser.add_argument('--cond_free', type=bool, default=None, help='Whether or not to perform conditioning-free diffusion. Conditioning-free diffusion performs two forward passes for each diffusion step: one with the outputs of the autoregressive model and one with no conditioning priors. The output of the two is blended according to the cond_free_k value below. Conditioning-free diffusion is the real deal, and dramatically improves realism.')
+    parser.add_argument('--cond_free_k', type=float, default=None, help='Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf]. As cond_free_k increases, the output becomes dominated by the conditioning-free signal. Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k')
+    parser.add_argument('--diffusion_temperature', type=float, default=None, help='Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0 are the "mean" prediction of the diffusion network and will sound bland and smeared. ')
+
     args = parser.parse_args()
     os.makedirs(args.output_path, exist_ok=True)
     tts = TextToSpeech(models_dir=args.model_dir, use_deepspeed=args.use_deepspeed)
@@ -34,8 +43,15 @@
             voice_sel = [selected_voice]
         voice_samples, conditioning_latents = load_voices(voice_sel)
 
-        gen, dbg_state = tts.tts_with_preset(args.text, k=args.candidates, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
-                                  preset=args.preset, use_deterministic_seed=args.seed, return_deterministic_state=True, cvvp_amount=args.cvvp_amount)
+#,seed="1234567", diffusion_temperature="0.2", top_p="1"
+        gen, dbg_state = tts.tts_with_preset(
+            args.text, k=args.candidates, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
+            preset=args.preset, use_deterministic_seed=args.seed, return_deterministic_state=True, cvvp_amount=args.cvvp_amount,
+            num_autoregressive_samples=args.num_autoregressive_samples, temperature=args.temperature,
+            length_penalty=args.length_penalty, repetition_penalty=args.repetition_penalty,
+            top_p=args.top_p, max_mel_tokens=args.max_mel_tokens, diffusion_iterations=args.diffusion_iterations,
+            cond_free=args.cond_free, cond_free_k=args.cond_free_k, diffusion_temperature=args.diffusion_temperature
+        )
         if isinstance(gen, list):
             for j, g in enumerate(gen):
                 torchaudio.save(os.path.join(args.output_path, f'{selected_voice}_{k}_{j}.wav'), g.squeeze(0).cpu(), 24000)