import os import shutil from pathlib import Path import streamlit as st from random import randint from tortoise.api import MODELS_DIR from tortoise.inference import ( infer_on_texts, run_and_save_tts, split_and_recombine_text, ) from tortoise.utils.diffusion import SAMPLERS from app_utils.filepicker import st_file_selector from app_utils.conf import TortoiseConfig from app_utils.funcs import ( timeit, load_model, list_voices, load_voice_conditionings, ) LATENT_MODES = [ "Tortoise original (bad)", "average per 4.27s (broken on small files)", "average per voice file (broken on small files)", ] def main(): conf = TortoiseConfig() voice_samples, conditioning_latents = None, None with st.expander("Create New Voice", expanded=True): if "file_uploader_key" not in st.session_state: st.session_state["file_uploader_key"] = str(randint(1000, 100000000)) st.session_state["text_input_key"] = str(randint(1000, 100000000)) uploaded_files = st.file_uploader( "Upload Audio Samples for a New Voice", accept_multiple_files=True, type=["wav"], key=st.session_state["file_uploader_key"] ) voice_name = st.text_input( "New Voice Name", help="Enter a name for your new voice.", value="", key=st.session_state["text_input_key"] ) create_voice_button = st.button( "Create Voice", disabled = ((voice_name.strip() == "") | (len(uploaded_files) == 0)) ) if create_voice_button: st.write(st.session_state) with st.spinner(f"Creating new voice: {voice_name}"): new_voice_name = voice_name.strip().replace(" ", "_") voices_dir = f'./tortoise/voices/{new_voice_name}/' if os.path.exists(voices_dir): shutil.rmtree(voices_dir) os.makedirs(voices_dir) for index, uploaded_file in enumerate(uploaded_files): bytes_data = uploaded_file.read() with open(f"{voices_dir}voice_sample{index}.wav", "wb") as wav_file: wav_file.write(bytes_data) # # Generate conditioning latents and samples here # voice_samples, conditioning_latents = generate_conditioning(voices_dir) # # Save the conditioning latents and samples # save_conditioning(voices_dir, voice_samples, conditioning_latents) st.session_state["text_input_key"] = str(randint(1000, 100000000)) st.session_state["file_uploader_key"] = str(randint(1000, 100000000)) st.experimental_rerun() text = st.text_area( "Text", help="Text to speak.", value="The expressiveness of autoregressive transformers is literally nuts! I absolutely adore them.", ) voices = [v for v in os.listdir("tortoise/voices") if v != "cond_latent_example"] voice = st.selectbox( "Voice", voices, 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.", index=0, ) preset = st.selectbox( "Preset", ( "single_sample", "ultra_fast", "very_fast", "ultra_fast_old", "fast", "standard", "high_quality", ), help="Which voice preset to use.", index=1, ) with st.expander("Advanced"): col1, col2 = st.columns(2) with col1: """#### Model parameters""" candidates = st.number_input( "Candidates", help="How many output candidates to produce per-voice.", value=1, ) latent_averaging_mode = st.radio( "Latent averaging mode", LATENT_MODES, help="How voice samples should be averaged together.", index=0, ) sampler = st.radio( "Sampler", ["dpm++2m", "p", "ddim"], help="Diffusion sampler. Note that dpm++2m is experimental and typically requires more steps.", index=1, ) steps = st.number_input( "Steps", help="Override the steps used for diffusion (default depends on preset)", value=10, ) seed = st.number_input( "Seed", help="Random seed which can be used to reproduce results.", value=-1, ) if seed == -1: seed = None voice_fixer = st.checkbox( "Voice fixer", help="Use `voicefixer` to improve audio quality. This is a post-processing step which can be applied to any output.", value=True, ) """#### Directories""" output_path = st.text_input( "Output Path", help="Where to store outputs.", value="results/" ) with col2: """#### Optimizations""" high_vram = not st.checkbox( "Low VRAM", help="Re-enable default offloading behaviour of tortoise", value=True, ) half = st.checkbox( "Half-Precision", help="Enable autocast to half precision for autoregressive model", value=False, ) kv_cache = st.checkbox( "Key-Value Cache", help="Enable kv_cache usage, leading to drastic speedups but worse memory usage", value=True, ) cond_free = st.checkbox( "Conditioning Free", help="Force conditioning free diffusion", value=True, ) no_cond_free = st.checkbox( "Force Not Conditioning Free", help="Force disable conditioning free diffusion", value=False, ) """#### Text Splitting""" min_chars_to_split = st.number_input( "Min Chars to Split", help="Minimum number of characters to split text on", min_value=50, value=200, step=1, ) """#### Debug""" produce_debug_state = st.checkbox( "Produce Debug State", help="Whether or not to produce debug_state.pth, which can aid in reproducing problems. Defaults to true.", value=True, ) ar_checkpoint = "." diff_checkpoint = "." if st.button("Update Basic Settings"): conf.update( EXTRA_VOICES_DIR=extra_voices_dir, LOW_VRAM=not high_vram, AR_CHECKPOINT=ar_checkpoint, DIFF_CHECKPOINT=diff_checkpoint, ) ar_checkpoint = None diff_checkpoint = None tts = load_model(MODELS_DIR, high_vram, kv_cache, ar_checkpoint, diff_checkpoint) if st.button("Start"): assert latent_averaging_mode assert preset assert voice def show_generation(fp, filename: str): """ audio_buffer = BytesIO() save_gen_with_voicefix(g, audio_buffer, squeeze=False) torchaudio.save(audio_buffer, g, 24000, format='wav') """ st.audio(str(fp), format="audio/wav") st.download_button( "Download sample", str(fp), file_name=filename, # this doesn't actually seem to work lol ) with st.spinner( f"Generating {candidates} candidates for voice {voice} (seed={seed}). You can see progress in the terminal" ): os.makedirs(output_path, exist_ok=True) selected_voices = voice.split(",") for k, selected_voice in enumerate(selected_voices): if "&" in selected_voice: voice_sel = selected_voice.split("&") else: voice_sel = [selected_voice] voice_samples, conditioning_latents = load_voice_conditionings( voice_sel, [] ) voice_path = Path(os.path.join(output_path, selected_voice)) with timeit( f"Generating {candidates} candidates for voice {selected_voice} (seed={seed})" ): nullable_kwargs = { k: v for k, v in zip( ["sampler", "diffusion_iterations", "cond_free"], [sampler, steps, cond_free], ) if v is not None } def call_tts(text: str): return tts.tts_with_preset( text, k=candidates, voice_samples=voice_samples, conditioning_latents=conditioning_latents, preset=preset, use_deterministic_seed=seed, return_deterministic_state=True, cvvp_amount=0.0, half=half, latent_averaging_mode=LATENT_MODES.index( latent_averaging_mode ), **nullable_kwargs, ) if len(text) < min_chars_to_split: filepaths = run_and_save_tts( call_tts, text, voice_path, return_deterministic_state=True, return_filepaths=True, voicefixer=voice_fixer, ) for i, fp in enumerate(filepaths): show_generation(fp, f"{selected_voice}-text-{i}.wav") else: desired_length = int(min_chars_to_split) texts = split_and_recombine_text( text, desired_length, desired_length + 100 ) filepaths = infer_on_texts( call_tts, texts, voice_path, return_deterministic_state=True, return_filepaths=True, lines_to_regen=set(range(len(texts))), voicefixer=voice_fixer, ) for i, fp in enumerate(filepaths): show_generation(fp, f"{selected_voice}-text-{i}.wav") if produce_debug_state: """Debug states can be found in the output directory""" if __name__ == "__main__": main()