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 os
import sys
from concurrent.futures import ThreadPoolExecutor


os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # for MPS device compatibility
sys.path.append(f"{os.path.dirname(os.path.abspath(__file__))}/../../third_party/BigVGAN/")

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
import requests
from huggingface_hub import hf_hub_download
from pydub import AudioSegment, silence
from transformers import pipeline
from vocos import Vocos

from f5_tts.model import CFM
from f5_tts.model.modules import MelSpec
from f5_tts.model.utils import convert_char_to_pinyin, get_tokenizer
from f5_tts.infer.cls_tokenizer_v2 import cls_tokenize_text


_ref_audio_cache = {}
_ref_text_cache = {}


def _load_state_dict_resilient(model: torch.nn.Module, state_dict: dict):
    """
    Load a state_dict while tolerating shape mismatches (e.g. when switching vocabularies).
    Any parameters whose shapes do not match the current model will be skipped so that
    those layers keep their freshly initialized weights instead of raising.
    """
    model_state = model.state_dict()
    filtered_state = {}
    mismatched = {}

    for key, weight in state_dict.items():
        target = model_state.get(key)
        if target is None:
            continue
        if hasattr(target, "shape") and hasattr(weight, "shape") and target.shape != weight.shape:
            # If only the vocab size dimension differs, align by slicing/padding so embeddings still load.
            if (
                len(target.shape) == len(weight.shape)
                and target.shape[1:] == weight.shape[1:]
                and key.endswith("text_embed.weight")
            ):
                new_weight = target.clone()
                rows = min(target.shape[0], weight.shape[0])
                new_weight[:rows] = weight[:rows]
                filtered_state[key] = new_weight
                if target.shape[0] != weight.shape[0]:
                    print(
                        f"Info: resized {key} from {tuple(weight.shape)} to {tuple(target.shape)} "
                        f"(copied {rows} rows)."
                    )
                continue
            mismatched[key] = (tuple(weight.shape), tuple(target.shape))
            continue
        filtered_state[key] = weight

    missing, unexpected = model.load_state_dict(filtered_state, strict=False)
    if mismatched:
        mismatch_info = ", ".join(f"{k}: {src} -> {dst}" for k, (src, dst) in mismatched.items())
        print(f"Warning: skipped loading parameters with shape mismatch ({mismatch_info}).")
    if missing:
        print(f"Warning: missing parameters not loaded from checkpoint: {missing}")
    if unexpected:
        print(f"Warning: unexpected parameters in checkpoint were ignored: {unexpected}")

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

tempfile_kwargs = {"delete_on_close": False} if sys.version_info >= (3, 12) else {"delete": False}

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

target_sample_rate = 24000
n_mel_channels = 100
hop_length = 256
win_length = 1024
n_fft = 1024
mel_spec_type = "vocos"
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


def tokenize_texts(
    text_list,
    tokenizer="pinyin",
    cls_language=None,
    cls_server_url=None,
    cls_timeout=5.0,
    cls_tokenizer_fn=None,
):
    tokenizer = (tokenizer or "pinyin").strip().lower()
    if tokenizer == "pinyin":
        return convert_char_to_pinyin(text_list)
    if tokenizer == "char":
        return [list(t) for t in text_list]
    if tokenizer == "cls":
        if not cls_language:
            raise ValueError("cls_language must be set when tokenizer='cls'.")
        if cls_tokenizer_fn is not None:
            results = []
            for text in text_list:
                cls_tokens = cls_tokenizer_fn(text, cls_language)
                if not isinstance(cls_tokens, list) or len(cls_tokens) == 0:
                    raise RuntimeError("CLS tokenizer function returned empty tokens.")
                results.append(cls_tokens)
            return results
        if cls_server_url:
            results = []
            for text in text_list:
                try:
                    resp = requests.post(
                        cls_server_url,
                        json={"text": text, "language": cls_language},
                        timeout=cls_timeout,
                    )
                except Exception as exc:  # noqa: BLE001
                    raise RuntimeError(f"CLS server request failed: {exc}") from exc
                if resp.status_code != 200:
                    raise RuntimeError(f"CLS server error {resp.status_code}: {resp.text}")
                try:
                    data = resp.json()
                except Exception as exc:  # noqa: BLE001
                    raise RuntimeError(f"CLS server returned non-JSON response: {exc}") from exc
                cls_tokens = data.get("cls")
                if not isinstance(cls_tokens, list) or len(cls_tokens) == 0:
                    raise RuntimeError("CLS server returned empty tokens.")
                results.append(cls_tokens)
            return results
        results = []
        for text in text_list:
            cls_tokens = cls_tokenize_text(text, cls_language)
            if not isinstance(cls_tokens, list) or len(cls_tokens) == 0:
                raise RuntimeError("CLS tokenizer returned empty tokens.")
            results.append(cls_tokens)
        return results
    raise ValueError(f"Unsupported tokenizer: {tokenizer}")


# load vocoder
def load_vocoder(vocoder_name="vocos", is_local=False, local_path="", device=device, hf_cache_dir=None):
    if vocoder_name == "vocos":
        # vocoder = Vocos.from_pretrained("charactr/vocos-mel-24khz").to(device)
        if is_local:
            print(f"Load vocos from local path {local_path}")
            config_path = f"{local_path}/config.yaml"
            model_path = f"{local_path}/pytorch_model.bin"
        else:
            print("Download Vocos from huggingface charactr/vocos-mel-24khz")
            repo_id = "charactr/vocos-mel-24khz"
            config_path = hf_hub_download(repo_id=repo_id, cache_dir=hf_cache_dir, filename="config.yaml")
            model_path = hf_hub_download(repo_id=repo_id, cache_dir=hf_cache_dir, filename="pytorch_model.bin")
        vocoder = Vocos.from_hparams(config_path)
        state_dict = torch.load(model_path, map_location="cpu", weights_only=True)
        from vocos.feature_extractors import EncodecFeatures

        if isinstance(vocoder.feature_extractor, EncodecFeatures):
            encodec_parameters = {
                "feature_extractor.encodec." + key: value
                for key, value in vocoder.feature_extractor.encodec.state_dict().items()
            }
            state_dict.update(encodec_parameters)
        vocoder.load_state_dict(state_dict)
        vocoder = vocoder.eval().to(device)
    elif vocoder_name == "bigvgan":
        try:
            from third_party.BigVGAN import bigvgan
        except ImportError:
            print("You need to follow the README to init submodule and change the BigVGAN source code.")
        if is_local:
            # download generator from https://huggingface.co/nvidia/bigvgan_v2_24khz_100band_256x/tree/main
            vocoder = bigvgan.BigVGAN.from_pretrained(local_path, use_cuda_kernel=False)
        else:
            vocoder = bigvgan.BigVGAN.from_pretrained(
                "nvidia/bigvgan_v2_24khz_100band_256x", use_cuda_kernel=False, cache_dir=hf_cache_dir
            )

        vocoder.remove_weight_norm()
        vocoder = vocoder.eval().to(device)
    return vocoder


# load asr pipeline

asr_pipe = None


def initialize_asr_pipeline(device: str = device, dtype=None):
    if dtype is None:
        dtype = (
            torch.float16
            if "cuda" in device
            and torch.cuda.get_device_properties(device).major >= 7
            and not torch.cuda.get_device_name().endswith("[ZLUDA]")
            else torch.float32
        )
    global asr_pipe
    asr_pipe = pipeline( 
        "automatic-speech-recognition",
        model="openai/whisper-large-v3-turbo",
        torch_dtype=dtype,
        device=device,
    )


# transcribe


def transcribe(ref_audio, language=None):
    global asr_pipe
    if asr_pipe is None:
        initialize_asr_pipeline(device=device)
    return asr_pipe(
        ref_audio,
        chunk_length_s=30,
        batch_size=128,
        generate_kwargs={"task": "transcribe", "language": language} if language else {"task": "transcribe"},
        return_timestamps=False,
    )["text"].strip()


# load model checkpoint for inference


def load_checkpoint(model, ckpt_path, device: str, dtype=None, use_ema=True):
    if dtype is None:
        dtype = torch.float32
        # dtype = (
        #     torch.float16
        #     if "cuda" in device
        #     and torch.cuda.get_device_properties(device).major >= 6
        #     and not torch.cuda.get_device_name().endswith("[ZLUDA]")
        #     else torch.float32
        # )
    model = model.to(dtype)

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

        checkpoint = load_file(ckpt_path, device=device)
    else:
        checkpoint = torch.load(ckpt_path, map_location=device, 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"]
        }

        # patch for backward compatibility, 305e3ea
        for key in ["mel_spec.mel_stft.mel_scale.fb", "mel_spec.mel_stft.spectrogram.window"]:
            if key in checkpoint["model_state_dict"]:
                del checkpoint["model_state_dict"][key]

        _load_state_dict_resilient(model, checkpoint["model_state_dict"])
    else:
        if ckpt_type == "safetensors":
            checkpoint = {"model_state_dict": checkpoint}
        _load_state_dict_resilient(model, checkpoint["model_state_dict"])

    del checkpoint
    torch.cuda.empty_cache()

    return model.to(device)


# load model for inference


def load_model(
    model_cls,
    model_cfg,
    ckpt_path,
    mel_spec_type=mel_spec_type,
    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("token : ", 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(
            n_fft=n_fft,
            hop_length=hop_length,
            win_length=win_length,
            n_mel_channels=n_mel_channels,
            target_sample_rate=target_sample_rate,
            mel_spec_type=mel_spec_type,
        ),
        odeint_kwargs=dict(
            method=ode_method,
        ),
        vocab_char_map=vocab_char_map,
    ).to(device)

    dtype = torch.float32 if mel_spec_type == "bigvgan" else None
    model = load_checkpoint(model, ckpt_path, device, dtype=dtype, use_ema=use_ema)

    return model


def remove_silence_edges(audio, silence_threshold=-42):
    # Remove silence from the start
    non_silent_start_idx = silence.detect_leading_silence(audio, silence_threshold=silence_threshold)
    audio = audio[non_silent_start_idx:]

    # Remove silence from the end
    non_silent_end_duration = audio.duration_seconds
    for ms in reversed(audio):
        if ms.dBFS > silence_threshold:
            break
        non_silent_end_duration -= 0.001
    trimmed_audio = audio[: int(non_silent_end_duration * 1000)]

    return trimmed_audio


# preprocess reference audio and text


def preprocess_ref_audio_text(ref_audio_orig, ref_text, show_info=print):
    show_info("Converting audio...")

    # Compute a hash of the reference audio file
    with open(ref_audio_orig, "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:
        show_info("Using cached preprocessed reference audio...")
        ref_audio = _ref_audio_cache[audio_hash]

    else:  # first pass, do preprocess
        with tempfile.NamedTemporaryFile(suffix=".wav", **tempfile_kwargs) as f:
            temp_path = f.name

        aseg = AudioSegment.from_file(ref_audio_orig)

        # 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, seek_step=10
        )
        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) > 12000:
                show_info("Audio is over 12s, 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) > 12000:
            non_silent_segs = silence.split_on_silence(
                aseg, min_silence_len=100, silence_thresh=-40, keep_silence=1000, seek_step=10
            )
            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) > 12000:
                    show_info("Audio is over 12s, 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) > 12000:
            aseg = aseg[:12000]
            show_info("Audio is over 12s, clipping short. (3)")

        aseg = remove_silence_edges(aseg) + AudioSegment.silent(duration=50)
        aseg.export(temp_path, format="wav")
        ref_audio = temp_path

        # Cache the processed reference audio
        _ref_audio_cache[audio_hash] = ref_audio

    if not ref_text.strip():
        global _ref_text_cache
        if audio_hash in _ref_text_cache:
            # Use cached asr transcription
            show_info("Using cached reference text...")
            ref_text = _ref_text_cache[audio_hash]
        else:
            show_info("No reference text provided, transcribing reference audio...")
            ref_text = transcribe(ref_audio)
            # Cache the transcribed text (not caching custom ref_text, enabling users to do manual tweak)
            _ref_text_cache[audio_hash] = ref_text
    else:
        show_info("Using custom reference 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 += ". "

    print("\nref_text  ", 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,
    mel_spec_type=mel_spec_type,
    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,
    tokenizer="pinyin",
    cls_language=None,
    cls_server_url=None,
    cls_timeout=5.0,
    cls_tokenizer_fn=None,
):
    # 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) * (22 - audio.shape[-1] / sr) * speed)
    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)
    print("\n")

    show_info(f"Generating audio in {len(gen_text_batches)} batches...")
    return next(
        infer_batch_process(
            (audio, sr),
            ref_text,
            gen_text_batches,
            model_obj,
            vocoder,
            mel_spec_type=mel_spec_type,
            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,
            tokenizer=tokenizer,
            cls_language=cls_language,
            cls_server_url=cls_server_url,
            cls_timeout=cls_timeout,
            cls_tokenizer_fn=cls_tokenizer_fn,
        )
    )


# infer batches


def infer_batch_process(
    ref_audio,
    ref_text,
    gen_text_batches,
    model_obj,
    vocoder,
    mel_spec_type="vocos",
    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,
    streaming=False,
    chunk_size=2048,
    tokenizer="pinyin",
    cls_language=None,
    cls_server_url=None,
    cls_timeout=5.0,
    cls_tokenizer_fn=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(orig_freq=sr, new_freq=target_sample_rate)
        audio = resampler(audio)
    audio = audio.to(device)

    mel_spectrogram = MelSpec(
        n_fft=n_fft,
        hop_length=hop_length,
        win_length=win_length,
        n_mel_channels=n_mel_channels,
        target_sample_rate=target_sample_rate,
        mel_spec_type=mel_spec_type,
    )
    ref_mel = mel_spectrogram(audio)
    if ref_mel.dim() == 3:
        ref_mel = ref_mel[0]
    ref_mel_len = ref_mel.shape[-1]

    generated_waves = []
    spectrograms = []

    if len(ref_text[-1].encode("utf-8")) == 1:
        ref_text = ref_text + " "

    def process_batch(gen_text):
        local_speed = speed
        if len(gen_text.encode("utf-8")) < 10:
            local_speed = 0.3

        # Prepare the text
        text_list = [ref_text + gen_text]
        final_text_list = tokenize_texts(
            text_list,
            tokenizer=tokenizer,
            cls_language=cls_language,
            cls_server_url=cls_server_url,
            cls_timeout=cls_timeout,
            cls_tokenizer_fn=cls_tokenizer_fn,
        )

        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 / local_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,
            )
            del _

            generated = generated.to(torch.float32)  # generated mel spectrogram [B, T, n_mel]
            gen = generated[0]

            # Align ref mel inside generated mel to find the boundary, then cut the robotic head.
            ref_mel_local = ref_mel[:, :ref_mel_len]
            gen_mel = gen.transpose(0, 1)  # [n_mel, T]
            if ref_mel_local.shape[0] != gen_mel.shape[0] and ref_mel_local.shape[1] == gen_mel.shape[0]:
                ref_mel_local = ref_mel_local.transpose(0, 1)
            cut_idx = 0
            if gen_mel.shape[1] > ref_mel_local.shape[1]:
                ref_len = ref_mel_local.shape[1]
                gen_unfold = gen_mel.unfold(1, ref_len, 1)  # [n_mel, T-ref_len+1, ref_len]
                diff = gen_unfold - ref_mel_local.unsqueeze(1)
                mse = torch.mean(diff * diff, dim=(0, 2))
                best = int(torch.argmin(mse).item())
                cut_idx = max(0, int((best + ref_len) * hop_length))

            gen = gen.unsqueeze(0)
            gen_mel_spec = gen.permute(0, 2, 1).to(torch.float32)
            if mel_spec_type == "vocos":
                generated_wave = vocoder.decode(gen_mel_spec).cpu()
            elif mel_spec_type == "bigvgan":
                generated_wave = vocoder(gen_mel_spec).squeeze(0).cpu()
            if rms < target_rms:
                generated_wave = generated_wave * rms / target_rms

            # wav -> numpy
            wave = generated_wave.squeeze(0).to(torch.float32)
            if cut_idx > 0 and cut_idx < wave.numel():
                wave = wave[cut_idx:]
            generated_wave = wave.unsqueeze(0)
            generated_wave = generated_wave.squeeze().cpu().numpy()

            if streaming:
                for j in range(0, len(generated_wave), chunk_size):
                    yield generated_wave[j : j + chunk_size], target_sample_rate
            else:
                generated_cpu = gen_mel_spec[0].cpu().numpy()
                del generated
                yield generated_wave, generated_cpu

    if streaming:
        for gen_text in progress.tqdm(gen_text_batches) if progress is not None else gen_text_batches:
            for chunk in process_batch(gen_text):
                yield chunk
    else:
        with ThreadPoolExecutor() as executor:
            futures = [executor.submit(process_batch, gen_text) for gen_text in gen_text_batches]
            for future in progress.tqdm(futures) if progress is not None else futures:
                result = future.result()
                if result:
                    generated_wave, generated_mel_spec = next(result)
                    generated_waves.append(generated_wave)
                    spectrograms.append(generated_mel_spec)

        if generated_waves:
            if cross_fade_duration <= 0:
                # Simply concatenate
                final_wave = np.concatenate(generated_waves)
            else:
                # Combine all generated waves with cross-fading
                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)

            yield final_wave, target_sample_rate, combined_spectrogram

        else:
            yield None, target_sample_rate, None


# 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, seek_step=10
    )
    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()