src/f5_tts/infer/utils_infer.py

# A unified script for inference process
# Make adjustments inside functions, and consider both gradio and cli scripts if need to change func output format

import hashlib
import re
import tempfile
from importlib.resources import files

import matplotlib

matplotlib.use("Agg")

import matplotlib.pylab as plt
import numpy as np
import torch
import torchaudio
import tqdm
from pydub import AudioSegment, silence
from transformers import pipeline
from vocos import Vocos

from f5_tts.model import CFM
from f5_tts.model.utils import (
    get_tokenizer,
    convert_char_to_pinyin,
)

_ref_audio_cache = {}

device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"

# -----------------------------------------

target_sample_rate = 24000
n_mel_channels = 100
hop_length = 256
target_rms = 0.1
cross_fade_duration = 0.15
ode_method = "euler"
nfe_step = 32  # 16, 32
cfg_strength = 2.0
sway_sampling_coef = -1.0
speed = 1.0
fix_duration = None

# -----------------------------------------


# chunk text into smaller pieces


def chunk_text(text, max_chars=135):
    """
    Splits the input text into chunks, each with a maximum number of characters.

    Args:
        text (str): The text to be split.
        max_chars (int): The maximum number of characters per chunk.

    Returns:
        List[str]: A list of text chunks.
    """
    chunks = []
    current_chunk = ""
    # Split the text into sentences based on punctuation followed by whitespace
    sentences = re.split(r"(?<=[;:,.!?])\s+|(?<=[；：，。！？])", text)

    for sentence in sentences:
        if len(current_chunk.encode("utf-8")) + len(sentence.encode("utf-8")) <= max_chars:
            current_chunk += sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence
        else:
            if current_chunk:
                chunks.append(current_chunk.strip())
            current_chunk = sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence

    if current_chunk:
        chunks.append(current_chunk.strip())

    return chunks


# load vocoder
def load_vocoder(is_local=False, local_path="", device=device):
    if is_local:
        print(f"Load vocos from local path {local_path}")
        vocos = Vocos.from_hparams(f"{local_path}/config.yaml")
        state_dict = torch.load(f"{local_path}/pytorch_model.bin", map_location=device)
        vocos.load_state_dict(state_dict)
        vocos.eval()
    else:
        print("Download Vocos from huggingface charactr/vocos-mel-24khz")
        vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
    return vocos


# load asr pipeline

asr_pipe = None


def initialize_asr_pipeline(device=device):
    global asr_pipe
    asr_pipe = pipeline(
        "automatic-speech-recognition",
        model="openai/whisper-large-v3-turbo",
        torch_dtype=torch.float16,
        device=device,
    )


# load model checkpoint for inference


def load_checkpoint(model, ckpt_path, device, use_ema=True):
    if device == "cuda":
        model = model.half()

    ckpt_type = ckpt_path.split(".")[-1]
    if ckpt_type == "safetensors":
        from safetensors.torch import load_file

        checkpoint = load_file(ckpt_path)
    else:
        checkpoint = torch.load(ckpt_path, weights_only=True)

    if use_ema:
        if ckpt_type == "safetensors":
            checkpoint = {"ema_model_state_dict": checkpoint}
        checkpoint["model_state_dict"] = {
            k.replace("ema_model.", ""): v
            for k, v in checkpoint["ema_model_state_dict"].items()
            if k not in ["initted", "step"]
        }
        model.load_state_dict(checkpoint["model_state_dict"])
    else:
        if ckpt_type == "safetensors":
            checkpoint = {"model_state_dict": checkpoint}
        model.load_state_dict(checkpoint["model_state_dict"])

    return model.to(device)


# load model for inference


def load_model(model_cls, model_cfg, ckpt_path, vocab_file="", ode_method=ode_method, use_ema=True, device=device):
    if vocab_file == "":
        vocab_file = str(files("f5_tts").joinpath("infer/examples/vocab.txt"))
    tokenizer = "custom"

    print("\nvocab : ", vocab_file)
    print("tokenizer : ", tokenizer)
    print("model : ", ckpt_path, "\n")

    vocab_char_map, vocab_size = get_tokenizer(vocab_file, tokenizer)
    model = CFM(
        transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
        mel_spec_kwargs=dict(
            target_sample_rate=target_sample_rate,
            n_mel_channels=n_mel_channels,
            hop_length=hop_length,
        ),
        odeint_kwargs=dict(
            method=ode_method,
        ),
        vocab_char_map=vocab_char_map,
    ).to(device)

    model = load_checkpoint(model, ckpt_path, device, use_ema=use_ema)

    return model


# preprocess reference audio and text


def preprocess_ref_audio_text(ref_audio_orig, ref_text, clip_short=True, show_info=print, device=device):
    show_info("Converting audio...")
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
        aseg = AudioSegment.from_file(ref_audio_orig)

        if clip_short:
            # 1. try to find long silence for clipping
            non_silent_segs = silence.split_on_silence(
                aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=1000
            )
            non_silent_wave = AudioSegment.silent(duration=0)
            for non_silent_seg in non_silent_segs:
                if len(non_silent_wave) > 6000 and len(non_silent_wave + non_silent_seg) > 15000:
                    show_info("Audio is over 15s, clipping short. (1)")
                    break
                non_silent_wave += non_silent_seg

            # 2. try to find short silence for clipping if 1. failed
            if len(non_silent_wave) > 15000:
                non_silent_segs = silence.split_on_silence(
                    aseg, min_silence_len=100, silence_thresh=-40, keep_silence=1000
                )
                non_silent_wave = AudioSegment.silent(duration=0)
                for non_silent_seg in non_silent_segs:
                    if len(non_silent_wave) > 6000 and len(non_silent_wave + non_silent_seg) > 15000:
                        show_info("Audio is over 15s, clipping short. (2)")
                        break
                    non_silent_wave += non_silent_seg

            aseg = non_silent_wave

            # 3. if no proper silence found for clipping
            if len(aseg) > 15000:
                aseg = aseg[:15000]
                show_info("Audio is over 15s, clipping short. (3)")

        aseg.export(f.name, format="wav")
        ref_audio = f.name

    # Compute a hash of the reference audio file
    with open(ref_audio, "rb") as audio_file:
        audio_data = audio_file.read()
        audio_hash = hashlib.md5(audio_data).hexdigest()

    global _ref_audio_cache
    if audio_hash in _ref_audio_cache:
        # Use cached reference text
        show_info("Using cached reference text...")
        ref_text = _ref_audio_cache[audio_hash]
    else:
        if not ref_text.strip():
            global asr_pipe
            if asr_pipe is None:
                initialize_asr_pipeline(device=device)
            show_info("No reference text provided, transcribing reference audio...")
            ref_text = asr_pipe(
                ref_audio,
                chunk_length_s=30,
                batch_size=128,
                generate_kwargs={"task": "transcribe"},
                return_timestamps=False,
            )["text"].strip()
            show_info("Finished transcription")
        else:
            show_info("Using custom reference text...")
        # Cache the transcribed text
        _ref_audio_cache[audio_hash] = ref_text

    # Ensure ref_text ends with a proper sentence-ending punctuation
    if not ref_text.endswith(". ") and not ref_text.endswith("。"):
        if ref_text.endswith("."):
            ref_text += " "
        else:
            ref_text += ". "

    return ref_audio, ref_text


# infer process: chunk text -> infer batches [i.e. infer_batch_process()]


def infer_process(
    ref_audio,
    ref_text,
    gen_text,
    model_obj,
    vocoder,
    show_info=print,
    progress=tqdm,
    target_rms=target_rms,
    cross_fade_duration=cross_fade_duration,
    nfe_step=nfe_step,
    cfg_strength=cfg_strength,
    sway_sampling_coef=sway_sampling_coef,
    speed=speed,
    fix_duration=fix_duration,
    device=device,
):
    # Split the input text into batches
    audio, sr = torchaudio.load(ref_audio)
    max_chars = int(len(ref_text.encode("utf-8")) / (audio.shape[-1] / sr) * (25 - audio.shape[-1] / sr))
    gen_text_batches = chunk_text(gen_text, max_chars=max_chars)
    for i, gen_text in enumerate(gen_text_batches):
        print(f"gen_text {i}", gen_text)

    show_info(f"Generating audio in {len(gen_text_batches)} batches...")
    return infer_batch_process(
        (audio, sr),
        ref_text,
        gen_text_batches,
        model_obj,
        vocoder,
        progress=progress,
        target_rms=target_rms,
        cross_fade_duration=cross_fade_duration,
        nfe_step=nfe_step,
        cfg_strength=cfg_strength,
        sway_sampling_coef=sway_sampling_coef,
        speed=speed,
        fix_duration=fix_duration,
        device=device,
    )


# infer batches


def infer_batch_process(
    ref_audio,
    ref_text,
    gen_text_batches,
    model_obj,
    vocoder,
    progress=tqdm,
    target_rms=0.1,
    cross_fade_duration=0.15,
    nfe_step=32,
    cfg_strength=2.0,
    sway_sampling_coef=-1,
    speed=1,
    fix_duration=None,
    device=None,
):
    audio, sr = ref_audio
    if audio.shape[0] > 1:
        audio = torch.mean(audio, dim=0, keepdim=True)

    rms = torch.sqrt(torch.mean(torch.square(audio)))
    if rms < target_rms:
        audio = audio * target_rms / rms
    if sr != target_sample_rate:
        resampler = torchaudio.transforms.Resample(sr, target_sample_rate)
        audio = resampler(audio)
    audio = audio.to(device)

    generated_waves = []
    spectrograms = []

    if len(ref_text[-1].encode("utf-8")) == 1:
        ref_text = ref_text + " "
    for i, gen_text in enumerate(progress.tqdm(gen_text_batches)):
        # Prepare the text
        text_list = [ref_text + gen_text]
        final_text_list = convert_char_to_pinyin(text_list)

        ref_audio_len = audio.shape[-1] // hop_length
        if fix_duration is not None:
            duration = int(fix_duration * target_sample_rate / hop_length)
        else:
            # Calculate duration
            ref_text_len = len(ref_text.encode("utf-8"))
            gen_text_len = len(gen_text.encode("utf-8"))
            duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed)

        # inference
        with torch.inference_mode():
            generated, _ = model_obj.sample(
                cond=audio,
                text=final_text_list,
                duration=duration,
                steps=nfe_step,
                cfg_strength=cfg_strength,
                sway_sampling_coef=sway_sampling_coef,
            )

        generated = generated.to(torch.float32)
        generated = generated[:, ref_audio_len:, :]
        generated_mel_spec = generated.permute(0, 2, 1)
        generated_wave = vocoder.decode(generated_mel_spec.cpu())
        if rms < target_rms:
            generated_wave = generated_wave * rms / target_rms

        # wav -> numpy
        generated_wave = generated_wave.squeeze().cpu().numpy()

        generated_waves.append(generated_wave)
        spectrograms.append(generated_mel_spec[0].cpu().numpy())

    # Combine all generated waves with cross-fading
    if cross_fade_duration <= 0:
        # Simply concatenate
        final_wave = np.concatenate(generated_waves)
    else:
        final_wave = generated_waves[0]
        for i in range(1, len(generated_waves)):
            prev_wave = final_wave
            next_wave = generated_waves[i]

            # Calculate cross-fade samples, ensuring it does not exceed wave lengths
            cross_fade_samples = int(cross_fade_duration * target_sample_rate)
            cross_fade_samples = min(cross_fade_samples, len(prev_wave), len(next_wave))

            if cross_fade_samples <= 0:
                # No overlap possible, concatenate
                final_wave = np.concatenate([prev_wave, next_wave])
                continue

            # Overlapping parts
            prev_overlap = prev_wave[-cross_fade_samples:]
            next_overlap = next_wave[:cross_fade_samples]

            # Fade out and fade in
            fade_out = np.linspace(1, 0, cross_fade_samples)
            fade_in = np.linspace(0, 1, cross_fade_samples)

            # Cross-faded overlap
            cross_faded_overlap = prev_overlap * fade_out + next_overlap * fade_in

            # Combine
            new_wave = np.concatenate(
                [prev_wave[:-cross_fade_samples], cross_faded_overlap, next_wave[cross_fade_samples:]]
            )

            final_wave = new_wave

    # Create a combined spectrogram
    combined_spectrogram = np.concatenate(spectrograms, axis=1)

    return final_wave, target_sample_rate, combined_spectrogram


# remove silence from generated wav


def remove_silence_for_generated_wav(filename):
    aseg = AudioSegment.from_file(filename)
    non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500)
    non_silent_wave = AudioSegment.silent(duration=0)
    for non_silent_seg in non_silent_segs:
        non_silent_wave += non_silent_seg
    aseg = non_silent_wave
    aseg.export(filename, format="wav")


# save spectrogram


def save_spectrogram(spectrogram, path):
    plt.figure(figsize=(12, 4))
    plt.imshow(spectrogram, origin="lower", aspect="auto")
    plt.colorbar()
    plt.savefig(path)
    plt.close()