added setting to adjust autoregressive sample batch size
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@ -145,6 +145,7 @@ Below are settings that override the default launch arguments. Some of these req
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* `Check for Updates`: checks for updates on page load and notifies in console. Only works if you pulled this repo from a gitea instance.
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* `Low VRAM`: disables optimizations in TorToiSe that increases VRAM consumption. Suggested if your GPU has under 6GiB.
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* `Voice Latent Max Chunk Size`: during the voice latents calculation pass, this limits how large, in bytes, a chunk can be. Large values can run into VRAM OOM errors.
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* `Sample Batch Size`: sets the batch size when generating autoregressive samples. Bigger batches result in faster compute, at the cost of increased VRAM consumption. Leave to 0 to calculate a "best" fit.
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* `Concurrency Count`: how many Gradio events the queue can process at once. Leave this over 1 if you want to modify settings in the UI that updates other settings while generating audio clips.
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Below are an explanation of experimental flags. Messing with these might impact performance, as these are exposed only if you know what you are doing.
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14
app.py
14
app.py
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@ -53,6 +53,7 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidate
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'cond_free_k': 2.0, 'diffusion_temperature': 1.0,
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'num_autoregressive_samples': num_autoregressive_samples,
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'sample_batch_size': args.sample_batch_size,
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'diffusion_iterations': diffusion_iterations,
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'voice_samples': voice_samples,
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@ -309,11 +310,12 @@ def check_for_updates():
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def update_voices():
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return gr.Dropdown.update(choices=os.listdir(os.listdir("./tortoise/voices")) + ["microphone"])
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def export_exec_settings( share, check_for_updates, low_vram, cond_latent_max_chunk_size, concurrency_count ):
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def export_exec_settings( share, check_for_updates, low_vram, cond_latent_max_chunk_size, sample_batch_size, concurrency_count ):
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args.share = share
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args.low_vram = low_vram
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args.check_for_updates = check_for_updates
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args.cond_latent_max_chunk_size = cond_latent_max_chunk_size
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args.sample_batch_size = sample_batch_size
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args.concurrency_count = concurrency_count
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settings = {
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@ -321,6 +323,7 @@ def export_exec_settings( share, check_for_updates, low_vram, cond_latent_max_ch
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'low-vram':args.low_vram,
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'check-for-updates':args.check_for_updates,
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'cond-latent-max-chunk-size': args.cond_latent_max_chunk_size,
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'sample-batch-size': args.sample_batch_size,
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'concurrency-count': args.concurrency_count,
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}
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@ -428,6 +431,7 @@ def main():
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exec_check_for_updates = gr.Checkbox(label="Check For Updates", value=args.check_for_updates)
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exec_arg_low_vram = gr.Checkbox(label="Low VRAM", value=args.low_vram)
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exec_arg_cond_latent_max_chunk_size = gr.Number(label="Voice Latents Max Chunk Size", precision=0, value=args.cond_latent_max_chunk_size)
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exec_arg_sample_batch_size = gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size)
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exec_arg_concurrency_count = gr.Number(label="Concurrency Count", precision=0, value=args.concurrency_count)
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@ -435,7 +439,7 @@ def main():
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check_updates_now = gr.Button(value="Check for Updates")
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exec_inputs = [exec_arg_share, exec_check_for_updates, exec_arg_low_vram, exec_arg_cond_latent_max_chunk_size, exec_arg_concurrency_count]
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exec_inputs = [exec_arg_share, exec_check_for_updates, exec_arg_low_vram, exec_arg_cond_latent_max_chunk_size, exec_arg_sample_batch_size, exec_arg_concurrency_count]
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for i in exec_inputs:
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i.change(
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@ -490,18 +494,22 @@ if __name__ == "__main__":
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'check-for-updates': False,
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'low-vram': False,
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'cond-latent-max-chunk-size': 1000000,
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'sample-batch-size': None,
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'concurrency-count': 3,
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}
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if os.path.isfile('./config/exec.json'):
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with open(f'./config/exec.json', 'r', encoding="utf-8") as f:
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default_arguments = json.load(f)
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overrides = json.load(f)
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for k in overrides:
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default_arguments[k] = overrides[k]
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parser = argparse.ArgumentParser()
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parser.add_argument("--share", action='store_true', default=default_arguments['share'], help="Lets Gradio return a public URL to use anywhere")
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parser.add_argument("--check-for-updates", action='store_true', default=default_arguments['check-for-updates'], help="Checks for update on startup")
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parser.add_argument("--low-vram", action='store_true', default=default_arguments['low-vram'], help="Disables some optimizations that increases VRAM usage")
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parser.add_argument("--cond-latent-max-chunk-size", default=default_arguments['cond-latent-max-chunk-size'], type=int, help="Sets an upper limit to audio chunk size when computing conditioning latents")
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parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets an upper limit to audio chunk size when computing conditioning latents")
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parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once")
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args = parser.parse_args()
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@ -391,6 +391,7 @@ class TextToSpeech:
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return_deterministic_state=False,
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# autoregressive generation parameters follow
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num_autoregressive_samples=512, temperature=.8, length_penalty=1, repetition_penalty=2.0, top_p=.8, max_mel_tokens=500,
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sample_batch_size=None,
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# CVVP parameters follow
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cvvp_amount=.0,
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# diffusion generation parameters follow
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@ -464,6 +465,8 @@ class TextToSpeech:
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diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k)
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self.autoregressive_batch_size = pick_best_batch_size_for_gpu() if sample_batch_size is None else sample_batch_size
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with torch.no_grad():
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samples = []
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num_batches = num_autoregressive_samples // self.autoregressive_batch_size
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