tortoise/inference.py

import os
import sys
from random import randint
from typing import List, Optional, Set, Union

from tortoise.utils.audio import get_voices, load_audio, load_voices
from tortoise.utils.text import split_and_recombine_text


def get_all_voices(extra_voice_dirs_str: str = ""):
    extra_voice_dirs = extra_voice_dirs_str.split(",") if extra_voice_dirs_str else []
    return sorted(get_voices(extra_voice_dirs)), extra_voice_dirs


def parse_voice_str(voice_str: str, all_voices: List[str]):
    selected_voices = all_voices if voice_str == "all" else voice_str.split(",")
    selected_voices = [v.split("&") if "&" in v else [v] for v in selected_voices]
    for voices in selected_voices:
        for v in voices:
            if v != "random" and v not in all_voices:
                raise ValueError(
                    f"voice {v} not available, use --list-voices to see available voices."
                )

    return selected_voices


def voice_loader(selected_voices: list, extra_voice_dirs: List[str]):
    for voices in selected_voices:
        yield voices, *load_voices(voices, extra_voice_dirs)


def parse_multiarg_text(text: List[str]):
    return (" ".join(text) if text else "".join(line for line in sys.stdin)).strip()


def split_text(text: str, text_split: str):
    if text_split:
        desired_length, max_length = map(int, text_split.split(","))
        if desired_length > max_length:
            raise ValueError(
                f"--text-split: desired_length ({desired_length}) must be <= max_length ({max_length})"
            )
        texts = split_and_recombine_text(text, desired_length, max_length)
    else:
        texts = split_and_recombine_text(text)
    #
    if not texts:
        raise ValueError("no text provided")
    return texts


def validate_output_dir(output_dir: str, selected_voices: list, candidates: int):
    if output_dir:
        os.makedirs(output_dir, exist_ok=True)
    else:
        if len(selected_voices) > 1:
            raise ValueError('cannot have multiple voices without --output-dir"')
        if candidates > 1:
            raise ValueError('cannot have multiple candidates without --output-dir"')
    return output_dir


def check_pydub(play: bool):
    if play:
        try:
            import pydub
            import pydub.playback

            return pydub
        except ImportError:
            raise RuntimeError(
                '--play requires pydub to be installed, which can be done with "pip install pydub"'
            )


def get_seed(seed: Optional[int]):
    return randint(0, 2**32 - 1) if seed is None else seed


from pathlib import Path
from typing import Any, Callable

import torch
import torchaudio


def run_and_save_tts(
    call_tts,
    text,
    output_dir: Path,
    return_deterministic_state,
    return_filepaths=False,
    voicefixer=True,
):
    output_dir.mkdir(exist_ok=True)
    if return_deterministic_state:
        gen, dbg = call_tts(text)
        torch.save(dbg, output_dir / "dbg.pt")
    else:
        gen = call_tts(text)
    #
    if not isinstance(gen, list):
        gen = [gen]
    gen = [g.squeeze(0).cpu() for g in gen]
    fps = []
    for i, g in enumerate(gen):
        fps.append(output_dir / f"{i}.wav")
        save_gen_with_voicefix(g, fps[-1], squeeze=False, voicefixer=voicefixer)
        # torchaudio.save(output_dir/f'{i}.wav', g, 24000)
    return fps if return_filepaths else gen


def infer_on_texts(
    call_tts: Callable[[str], Any],
    texts: List[str],
    output_dir: Union[str, Path],
    return_deterministic_state: bool,
    lines_to_regen: Set[int],
    logger=print,
    return_filepaths=False,
    voicefixer=True,
):
    audio_chunks = []
    base_p = Path(output_dir)
    base_p.mkdir(exist_ok=True)

    for text_idx, text in enumerate(texts):
        line_p = base_p / f"{text_idx}"
        line_p.mkdir(exist_ok=True)
        #
        if text_idx not in lines_to_regen:
            files = list(line_p.glob("*.wav"))
            if files:
                logger(f"loading existing audio fragments for [{text_idx}]")
                audio_chunks.append([load_audio(str(f), 24000) for f in files])
                continue
            else:
                logger(f"no existing audio fragment for [{text_idx}]")
        #
        logger(f"generating audio for text {text_idx}: {text}")
        audio_chunks.append(
            run_and_save_tts(
                call_tts,
                text,
                line_p,
                return_deterministic_state,
                voicefixer=voicefixer,
            )
        )

    fnames = []
    results = []
    for i in range(len(audio_chunks[0])):
        resultant = torch.cat([c[i] for c in audio_chunks], dim=-1)
        fnames.append(base_p / f"combined-{i}.wav")
        save_gen_with_voicefix(
            resultant, fnames[-1], squeeze=False, voicefixer=False
        )  # do not run fix on combined!!
        results.append(resultant)
        # torchaudio.save(base_p/'combined.wav', resultant, 24000)
    return fnames if return_filepaths else results


from voicefixer import VoiceFixer

vfixer = VoiceFixer()


def save_gen_with_voicefix(g, fpath, squeeze=True, voicefixer=True):
    torchaudio.save(fpath, g.squeeze(0).cpu() if squeeze else g, 24000, format="wav")
    if voicefixer:
        vfixer.restore(
            input=fpath,
            output=fpath,
            cuda=True,
            mode=0,
            # your_vocoder_func = convert_mel_to_wav # TODO test if integration with unvinet improves things
        )