import re
import gradio as gr
import numpy as np
import tempfile
from tqdm import tqdm
from einops import rearrange
from pydub import AudioSegment, silence
from model import UNetT, DiT
from cached_path import cached_path
from model.utils import (
    get_tokenizer,
    convert_char_to_pinyin,
)
from infer.utils_infer import (
    load_vocoder,
    load_model,
    remove_silence_edges,
    remove_silence_for_generated_wav,
    save_spectrogram,
)
from tokenizers import Tokenizer
from phonemizer import phonemize

from transformers import pipeline
import soundfile as sf

# Import PyTorch and torchaudio
import torch
import torchaudio

# Force CPU device for CPU Basic (free tier)
device = torch.device("cpu")
dtype = torch.float32
print(f"Using device: {device}, dtype: {dtype}")

pipe = pipeline(
    "automatic-speech-recognition",
    model="openai/whisper-small",
    torch_dtype=dtype,
    device=device,
)
vocos = load_vocoder()
# --------------------- Settings -------------------- #

target_sample_rate = 24000
n_mel_channels = 100
hop_length = 256
target_rms = 0.1
nfe_step = 16
cfg_strength = 2.0
ode_method = "euler"
sway_sampling_coef = -1.0
speed = 1
fix_duration = None
ref_language = "en-us"
language = "en-us"


DEFAULT_TTS_MODEL = "F5-TTS"
tts_model_choice = DEFAULT_TTS_MODEL


# load models
F5TTS_model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)


def load_custom(ckpt_path: str, vocab_path="", model_cfg=None):
    ckpt_path, vocab_path = ckpt_path.strip(), vocab_path.strip()
    if ckpt_path.startswith("hf://"):
        ckpt_path = str(cached_path(ckpt_path))
    if vocab_path.startswith("hf://"):
        vocab_path = str(cached_path(vocab_path))
    if model_cfg is None:
        model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
    return load_model(DiT, model_cfg, ckpt_path, vocab_file=vocab_path)


custom_ema_model, pre_custom_path = None, ""

chat_model_state = None
chat_tokenizer_state = None


# load models
F5TTS_ema_model = load_custom(
    "hf://Gregniuki/F5-tts_English_German_Polish/multi3/model_900000.pt", "", F5TTS_model_cfg
)


def chunk_text(text, max_chars):
    """
    Splits the input text into chunks, ensuring:
    - Chunks are split by punctuation where possible.
    - If no punctuation is found and the chunk exceeds `split_after_space_chars`,
      it is split into smaller chunks of up to `split_after_space_chars`.

    Args:
        text (str): The text to be split.
        max_chars (int): The maximum number of characters per chunk after punctuation.
        split_after_space_chars (int): The maximum number of characters per chunk when no punctuation is present.

    Returns:
        List[str]: A list of text chunks.
    """
    if max_chars > 135:
        max_chars = 135
    if max_chars < 50:
        max_chars = 50

    split_after_space_chars = max_chars + int(max_chars * 0.1)
    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 adding this sentence doesn't exceed max_chars, append it to the current chunk
        if len(current_chunk) + len(sentence) + 1 <= max_chars:  # +1 for space
            current_chunk += sentence + " "
        else:
            # If current chunk exceeds split_after_space_chars, handle the splitting
            while len(current_chunk) > split_after_space_chars:
                split_index = current_chunk.rfind(" ", 0, split_after_space_chars)
                if split_index == -1:  # No spaces to split; force split at 135 characters
                    split_index = split_after_space_chars
                chunks.append(current_chunk[:split_index].strip())
                current_chunk = current_chunk[split_index:].strip()

            # Add the current chunk to the list and start a new chunk
            if current_chunk:
                chunks.append(current_chunk.strip())
            current_chunk = sentence + " "

    # If the remaining chunk exceeds split_after_space_chars, split it further
    while len(current_chunk) > split_after_space_chars:
        split_index = current_chunk.rfind(" ", 0, split_after_space_chars)
        if split_index == -1:  # No spaces to split; force split at 135 characters
            split_index = split_after_space_chars
        chunks.append(current_chunk[:split_index].strip())
        current_chunk = current_chunk[split_index:].strip()

    # Add any leftover chunk
    if current_chunk:
        chunks.append(current_chunk.strip())

    return chunks


def text_to_ipa(text, language=language):
    try:
        ipa_text = phonemize(
            text,
            language=language,
            backend='espeak',
            strip=False,
            preserve_punctuation=True,
            with_stress=True
        )
        # Remove language markings like (en), (cmn), (de), (pl), (ru)
        ipa_text = re.sub(r'\([a-z]{2,3}\)', '', ipa_text)
        ipa_text = re.sub(r'tʃˈaɪniːzlˈe̞tə', '', ipa_text)
        ipa_text = re.sub(r'tʃˈaɪniːzɭˈetə', '', ipa_text)
        ipa_text = re.sub(r'dʒˈapəniːzlˈe̞tə', '', ipa_text)
        ipa_text = re.sub(r'dʒˈapəniːzɭˈetə', '', ipa_text)
        return ipa_text
    except Exception as e:
        print(f"Error processing text: {text}. Error: {e}")
        return None


def infer_batch(ref_audio, ref_text, gen_text_batches, exp_name, remove_silence, cross_fade_duration=0.15, progress=gr.Progress()):
    if exp_name == "Multi":
        ema_model = F5TTS_ema_model

    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)
    tokenizer = Tokenizer.from_file("data/Emilia_ZH_EN_pinyin/tokenizer.json")
    vocab_size = tokenizer.get_vocab_size()
    vocab = tokenizer.get_vocab()

    generated_waves = []
    spectrograms = []

    punctuation_weights = {",": 0, ".": 0, " ": 0}
    progress = tqdm(gen_text_batches)
    ipa_text_ref = text_to_ipa(ref_text, language=ref_language)
    print(ref_language)
    print(language)

    for i, gen_text in enumerate(progress):

        ipa_text_gen = text_to_ipa(gen_text, language=language)
        print(ipa_text_gen)

        text_list = ipa_text_ref + ipa_text_gen
        print(text_list)
        encoding = tokenizer.encode(text_list)
        tokens = encoding.tokens
        text_list = ' '.join(map(str, tokens))

        final_text_list = [text_list]
        print(final_text_list)

        # Calculate reference audio length
        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 text lengths with weights
            def calculate_weighted_length(text):
                length = len(text.encode("utf-8"))
                additional_length = sum(punctuation_weights.get(char, 0) for char in text)
                return length + additional_length

            ref_text_len = calculate_weighted_length(ref_text)
            gen_text_len = calculate_weighted_length(gen_text)

            duration = max(250, int(ref_audio_len) + int(((ref_audio_len / ref_text_len) * gen_text_len) / speed))

        print(f"Chunk {i + 1}: Duration: {duration} speed {speed}")

        # inference
        with torch.inference_mode():
            audio = audio.to(ema_model.device)
            final_text_list = [t.to(ema_model.device) if isinstance(t, torch.Tensor) else t for t in final_text_list]
            generated, _ = ema_model.sample(
                cond=audio,
                text=final_text_list,
                duration=duration,
                steps=nfe_step,
                cfg_strength=cfg_strength,
                sway_sampling_coef=sway_sampling_coef,
            )

        # Process generated tensor
        generated = generated[:, ref_audio_len:, :]
        generated_mel_spec = rearrange(generated, "1 n d -> 1 d n")

        # Convert to float32 for vocos on CPU
        generated_mel_spec = generated_mel_spec.to(dtype=torch.float32)
        generated_wave = vocos.decode(generated_mel_spec)

        # Adjust wave RMS if needed
        if rms < target_rms:
            generated_wave = generated_wave * rms / target_rms

        # Convert to 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:
        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]

            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:
                final_wave = np.concatenate([prev_wave, next_wave])
                continue

            prev_overlap = prev_wave[-cross_fade_samples:]
            next_overlap = next_wave[:cross_fade_samples]

            fade_out = np.linspace(1, 0, cross_fade_samples)
            fade_in = np.linspace(0, 1, cross_fade_samples)

            cross_faded_overlap = prev_overlap * fade_out + next_overlap * fade_in

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

            final_wave = new_wave

    # Remove silence
    if remove_silence:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
            final_wave_float32 = final_wave.astype(np.float32)
            sf.write(f.name, final_wave_float32, target_sample_rate)
            aseg = AudioSegment.from_file(f.name)
            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(f.name, format="wav")
            final_wave_np, _ = sf.read(f.name)
            final_wave = torch.from_numpy(final_wave_np).float()
            if final_wave.dim() == 1:
                final_wave = final_wave.unsqueeze(0)
            else:
                final_wave = final_wave.T
        final_wave = final_wave.squeeze().cpu().numpy()

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

    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_spectrogram:
        spectrogram_path = tmp_spectrogram.name
        save_spectrogram(combined_spectrogram, spectrogram_path)

    return (target_sample_rate, final_wave), spectrogram_path

def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, cross_fade_duration=0.15):

    print(gen_text)

    gr.Info("Converting audio...")
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
        aseg = AudioSegment.from_file(ref_audio_orig)
        aseg = remove_silence_edges(aseg) + AudioSegment.silent(duration=150)
        non_silent_segs = silence.split_on_silence(
            aseg, min_silence_len=700, silence_thresh=-50, keep_silence=700
        )
        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

        audio_duration = len(aseg)
        if audio_duration > 10000:
            gr.Warning("Audio is over 10s, clipping to only first 10s.")
            aseg = aseg[:10000]
        aseg.export(f.name, format="wav")
        ref_audio = f.name

    if not ref_text or not ref_text.strip():
        gr.Info("No reference text provided, transcribing reference audio...")

        ref_text = pipe(
            ref_audio,
            chunk_length_s=15,
            batch_size=8,
            generate_kwargs={"task": "transcribe"},
            return_timestamps=False,
        )["text"].strip()
        gr.Info("Finished transcription")
    else:
        gr.Info("Using custom reference text...")

    # Add the functionality to ensure it ends with ". "
    if not ref_text.endswith(". "):
        if ref_text.endswith("."):
            ref_text += " "
        else:
            ref_text += ". "

    audio_np, sr = sf.read(ref_audio)
    audio = torch.from_numpy(audio_np).float()
    if audio.dim() == 1:
        audio = audio.unsqueeze(0)
    else:
        audio = audio.T  # soundfile: (frames, channels) -> torchaudio: (channels, frames)

    max_chars = int((len(ref_text.encode('utf-8')) / (audio.shape[-1] / sr) * (20 - audio.shape[-1] / sr )))
    print(f"text: {max_chars} ")
    gen_text_batches = chunk_text(gen_text, max_chars=max_chars)
    print('ref_text', ref_text)
    for i, batch_text in enumerate(gen_text_batches):
        print(f'gen_text {i}', batch_text)

    gr.Info(f"Generating audio using {exp_name} in {len(gen_text_batches)} batches")
    return infer_batch((audio, sr), ref_text, gen_text_batches, exp_name, remove_silence, cross_fade_duration)


def generate_podcast(script, speaker1_name, ref_audio1, ref_text1, speaker2_name, ref_audio2, ref_text2, exp_name, remove_silence):
    speaker_pattern = re.compile(f"^({re.escape(speaker1_name)}|{re.escape(speaker2_name)}):", re.MULTILINE)
    speaker_blocks = speaker_pattern.split(script)[1:]

    generated_audio_segments = []

    for i in range(0, len(speaker_blocks), 2):
        speaker = speaker_blocks[i]
        text = speaker_blocks[i+1].strip()

        if speaker == speaker1_name:
            ref_audio = ref_audio1
            ref_text = ref_text1
        elif speaker == speaker2_name:
            ref_audio = ref_audio2
            ref_text = ref_text2
        else:
            continue

        audio, _ = infer(ref_audio, ref_text, text, exp_name, remove_silence)

        sr, audio_data = audio

        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
            sf.write(temp_file.name, audio_data, sr)
            audio_segment = AudioSegment.from_wav(temp_file.name)

        generated_audio_segments.append(audio_segment)

        pause = AudioSegment.silent(duration=500)
        generated_audio_segments.append(pause)

    final_podcast = sum(generated_audio_segments)

    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
        podcast_path = temp_file.name
        final_podcast.export(podcast_path, format="wav")

    return podcast_path

def parse_speechtypes_text(gen_text):
    pattern = r'\((.*?)\)'

    tokens = re.split(pattern, gen_text)

    segments = []

    current_emotion = 'Regular'

    for i in range(len(tokens)):
        if i % 2 == 0:
            text = tokens[i].strip()
            if text:
                segments.append({'emotion': current_emotion, 'text': text})
        else:
            emotion = tokens[i].strip()
            current_emotion = emotion

    return segments

def update_language(new_language):
    global language
    language = new_language

def update_language1(new_ref_language):
    global ref_language
    ref_language = new_ref_language

def update_speed(new_speed):
    global speed
    speed = new_speed
    return f"Speed set to: {speed}"

with gr.Blocks() as app_credits:
    gr.Markdown("""
    # Credits
    * [mrfakename](https://github.com/fakerybakery) for the original [online demo](https://huggingface.co/spaces/mrfakename/E2-F5-TTS)
    * [RootingInLoad](https://github.com/RootingInLoad) for the podcast generation
    * [jpgallegoar](https://github.com/jpgallegoar) for multiple speech-type generation
    """)

with gr.Blocks() as app_tts:
    gr.Markdown("# Batched TTS")
    ref_audio_input = gr.Audio(label="Reference Audio", type="filepath")
    gen_text_input = gr.Textbox(label="Text to Generate", lines=10)
    model_choice = gr.Radio(
        choices=["Multi"], label="Choose TTS Model", value="Multi"
    )
    gr.Markdown("#Select Reference Language")
    language_choice1 = gr.Dropdown(
        choices=["af", "nl", "en-us", "en-gb", "en-029", "en-gb-x-gbclan", "en-gb-x-rp", "en-gb-scotland", "en-gb-x-gbcwmd", "de", "lb",
                 "my", "yue", "hak", "cmn",
                 "an", "ca", "fr-be", "fr-fr", "fr-ch", "ht", "it", "pap", "pt-br", "pt", "ro", "es", "es-419",
                 "as", "bn", "bpy", "gu", "hi", "kok", "mr", "ne", "or", "pa", "sd", "si", "ur",
                 "am", "ar", "he", "mt",
                 "be", "ru", "ru-lv", "uk",
                 "ja",
                 "ko",
                 "id", "mi", "ms",
                 "az", "ba", "cu", "kk", "ky", "nog", "tk", "tt", "tr", "ug", "uz",
                 "vi-vn-x-central", "vi", "vi-vn-x-south",
                 "kn", "ml", "ta", "te",
                 "shn", "th",
                 "cs", "pl", "sk",
                 "da", "is", "nb", "sv",
                 "fa", "fa-latn", "ku",
                 "tn", "sw",
                 "grc", "el",
                 "et", "fi", "hu", "smj",
                 "bs", "bg", "hr", "mk", "sr", "sl",
                 "sq", "hy", "hyw",
                 "om",
                 "ka",
                 "ga", "gd", "cy",
                 "ltg", "lv", "lt",
                 "gn",
                 "quc", "qu",
                 "nci",
                 "kl",
                 "chr",
                 "haw",
                 "la",
                 "eo", "ia", "io", "lfn", "jbo", "py", "qdb", "qya", "piqd", "sjn"
                ], label="Choose Language", value="en-us"
    )
    gr.Markdown("#Select Synthesized Language")
    language_choice = gr.Dropdown(
        choices=["af", "nl", "en-us", "en-gb", "en-029", "en-gb-x-gbclan", "en-gb-x-rp", "en-gb-scotland", "en-gb-x-gbcwmd", "de", "lb",
                 "my", "yue", "hak", "cmn",
                 "an", "ca", "fr-be", "fr-fr", "fr-ch", "ht", "it", "pap", "pt-br", "pt", "ro", "es", "es-419",
                 "as", "bn", "bpy", "gu", "hi", "kok", "mr", "ne", "or", "pa", "sd", "si", "ur",
                 "am", "ar", "he", "mt",
                 "be", "ru", "ru-lv", "uk",
                 "ja",
                 "ko",
                 "id", "mi", "ms",
                 "az", "ba", "cu", "kk", "ky", "nog", "tk", "tt", "tr", "ug", "uz",
                 "vi-vn-x-central", "vi", "vi-vn-x-south",
                 "kn", "ml", "ta", "te",
                 "shn", "th",
                 "cs", "pl", "sk",
                 "da", "is", "nb", "sv",
                 "fa", "fa-latn", "ku",
                 "tn", "sw",
                 "grc", "el",
                 "et", "fi", "hu", "smj",
                 "bs", "bg", "hr", "mk", "sr", "sl",
                 "sq", "hy", "hyw",
                 "om",
                 "ka",
                 "ga", "gd", "cy",
                 "ltg", "lv", "lt",
                 "gn",
                 "quc", "qu",
                 "nci",
                 "kl",
                 "chr",
                 "haw",
                 "la",
                 "eo", "ia", "io", "lfn", "jbo", "py", "qdb", "qya", "piqd", "sjn"
                ], label="Choose Language", value="en-us"
    )
    generate_btn = gr.Button("Synthesize", variant="primary")
    with gr.Accordion("Advanced Settings", open=False):
        ref_text_input = gr.Textbox(
            label="Reference Text",
            info="Leave blank to automatically transcribe the reference audio. If you enter text it will override automatic transcription.",
            lines=2,
        )
        remove_silence = gr.Checkbox(
            label="Remove Silences",
            info="The model tends to produce silences, especially on longer audio. We can manually remove silences if needed. Note that this is an experimental feature and may produce strange results. This will also increase generation time.",
            value=False,
        )
        speed_slider = gr.Slider(
            label="Speed",
            minimum=0.3,
            maximum=2.0,
            value=1.0,
            step=0.1,
            info="Adjust the speed of the audio.",
        )
        cross_fade_duration_slider = gr.Slider(
            label="Cross-Fade Duration (s)",
            minimum=0.0,
            maximum=1.0,
            value=0.15,
            step=0.01,
            info="Set the duration of the cross-fade between audio clips.",
        )
    language_status = gr.Textbox(label="Current Language", interactive=False)
    ref_language_status = gr.Textbox(label="Reference Language", interactive=False)
    speed_slider.change(update_speed, inputs=speed_slider)
    language_choice.change(update_language, inputs=language_choice, outputs=language_status)
    language_choice1.change(update_language1, inputs=language_choice1, outputs=ref_language_status)


    audio_output = gr.Audio(label="Synthesized Audio")
    spectrogram_output = gr.Image(label="Spectrogram")

    generate_btn.click(
        infer,
        inputs=[
            ref_audio_input,
            ref_text_input,
            gen_text_input,
            model_choice,
            remove_silence,
            cross_fade_duration_slider,
        ],
        outputs=[audio_output, spectrogram_output],
    )

def parse_emotional_text(gen_text):
    pattern = r'\((.*?)\)'

    tokens = re.split(pattern, gen_text)

    segments = []

    current_emotion = 'Regular'

    for i in range(len(tokens)):
        if i % 2 == 0:
            text = tokens[i].strip()
            if text:
                segments.append({'emotion': current_emotion, 'text': text})
        else:
            emotion = tokens[i].strip()
            current_emotion = emotion

    return segments

with gr.Blocks() as app_emotional:
    gr.Markdown(
        """
    # Multiple Speech-Type Generation

    This section allows you to upload different audio clips for each speech type. 'Regular' emotion is mandatory. You can add additional speech types by clicking the "Add Speech Type" button. Enter your text in the format shown below, and the system will generate speech using the appropriate emotions. If unspecified, the model will use the regular speech type. The current speech type will be used until the next speech type is specified.

    **Example Input:**

    (Regular) Hello, I'd like to order a sandwich please. (Surprised) What do you mean you're out of bread? (Sad) I really wanted a sandwich though... (Angry) You know what, darn you and your little shop, you suck! (Whisper) I'll just go back home and cry now. (Shouting) Why me?!
    """
    )

    gr.Markdown("Upload different audio clips for each speech type. 'Regular' emotion is mandatory. You can add additional speech types by clicking the 'Add Speech Type' button.")

    with gr.Row():
        regular_name = gr.Textbox(value='Regular', label='Speech Type Name', interactive=False)
        regular_audio = gr.Audio(label='Regular Reference Audio', type='filepath')
        regular_ref_text = gr.Textbox(label='Reference Text (Regular)', lines=2)

    max_speech_types = 10
    speech_type_names = []
    speech_type_audios = []
    speech_type_ref_texts = []
    speech_type_delete_btns = []

    for i in range(max_speech_types - 1):
        with gr.Row():
            name_input = gr.Textbox(label='Speech Type Name', visible=False)
            audio_input = gr.Audio(label='Reference Audio', type='filepath', visible=False)
            ref_text_input = gr.Textbox(label='Reference Text', lines=2, visible=False)
            delete_btn = gr.Button("Delete", variant="secondary", visible=False)
        speech_type_names.append(name_input)
        speech_type_audios.append(audio_input)
        speech_type_ref_texts.append(ref_text_input)
        speech_type_delete_btns.append(delete_btn)

    add_speech_type_btn = gr.Button("Add Speech Type")

    speech_type_count = gr.State(value=0)

    def add_speech_type_fn(speech_type_count):
        if speech_type_count < max_speech_types - 1:
            speech_type_count += 1
            name_updates = []
            audio_updates = []
            ref_text_updates = []
            delete_btn_updates = []
            for i in range(max_speech_types - 1):
                if i < speech_type_count:
                    name_updates.append(gr.update(visible=True))
                    audio_updates.append(gr.update(visible=True))
                    ref_text_updates.append(gr.update(visible=True))
                    delete_btn_updates.append(gr.update(visible=True))
                else:
                    name_updates.append(gr.update())
                    audio_updates.append(gr.update())
                    ref_text_updates.append(gr.update())
                    delete_btn_updates.append(gr.update())
        else:
            name_updates = [gr.update() for _ in range(max_speech_types - 1)]
            audio_updates = [gr.update() for _ in range(max_speech_types - 1)]
            ref_text_updates = [gr.update() for _ in range(max_speech_types - 1)]
            delete_btn_updates = [gr.update() for _ in range(max_speech_types - 1)]
        return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates

    add_speech_type_btn.click(
        add_speech_type_fn,
        inputs=speech_type_count,
        outputs=[speech_type_count] + speech_type_names + speech_type_audios + speech_type_ref_texts + speech_type_delete_btns
    )

    def make_delete_speech_type_fn(index):
        def delete_speech_type_fn(speech_type_count):
            name_updates = []
            audio_updates = []
            ref_text_updates = []
            delete_btn_updates = []

            for i in range(max_speech_types - 1):
                if i == index:
                    name_updates.append(gr.update(visible=False, value=''))
                    audio_updates.append(gr.update(visible=False, value=None))
                    ref_text_updates.append(gr.update(visible=False, value=''))
                    delete_btn_updates.append(gr.update(visible=False))
                else:
                    name_updates.append(gr.update())
                    audio_updates.append(gr.update())
                    ref_text_updates.append(gr.update())
                    delete_btn_updates.append(gr.update())

            speech_type_count = max(0, speech_type_count - 1)

            return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates

        return delete_speech_type_fn

    for i, delete_btn in enumerate(speech_type_delete_btns):
        delete_fn = make_delete_speech_type_fn(i)
        delete_btn.click(
            delete_fn,
            inputs=speech_type_count,
            outputs=[speech_type_count] + speech_type_names + speech_type_audios + speech_type_ref_texts + speech_type_delete_btns
        )

    gen_text_input_emotional = gr.Textbox(label="Text to Generate", lines=10)

    model_choice_emotional = gr.Radio(
        choices=["Multi"], label="Choose TTS Model", value="Multi"
    )

    with gr.Accordion("Advanced Settings", open=False):
        remove_silence_emotional = gr.Radio(
            choices=["True", "False"],
            label="Remove Silences",
            value="False",
            info="Manually remove silences (experimental).",
        )
    generate_emotional_btn = gr.Button("Generate Emotional Speech", variant="primary")

    audio_output_emotional = gr.Audio(label="Synthesized Audio")

    def generate_emotional_speech(
        regular_audio,
        regular_ref_text,
        gen_text,
        *args,
    ):

        num_additional_speech_types = max_speech_types - 1
        speech_type_names_list = args[0:num_additional_speech_types]
        speech_type_audios_list = args[num_additional_speech_types : 2 * num_additional_speech_types]
        speech_type_ref_texts_list = args[2 * num_additional_speech_types : 3 * num_additional_speech_types]
        model_choice = args[3 * num_additional_speech_types]
        remove_silence_str = args[3 * num_additional_speech_types + 1]
        remove_silence = remove_silence_str == "True"

        speech_types = {'Regular': {'audio': regular_audio, 'ref_text': regular_ref_text}}

        for name_input, audio_input, ref_text_input in zip(speech_type_names_list, speech_type_audios_list, speech_type_ref_texts_list):
            if name_input and audio_input:
                speech_types[name_input] = {'audio': audio_input, 'ref_text': ref_text_input}

        segments = parse_speechtypes_text(gen_text)

        generated_audio_segments = []
        current_emotion = 'Regular'

        for segment in segments:
            emotion = segment['emotion']
            text = segment['text']

            if emotion in speech_types:
                current_emotion = emotion
            else:
                current_emotion = 'Regular'

            ref_audio = speech_types[current_emotion]['audio']
            ref_text = speech_types[current_emotion].get('ref_text', '')

            audio, _ = infer(ref_audio, ref_text, text, model_choice, remove_silence)
            sr, audio_data = audio

            generated_audio_segments.append(audio_data)

        if generated_audio_segments:
            final_audio_data = np.concatenate(generated_audio_segments)
            return (sr, final_audio_data)
        else:
            gr.Warning("No audio generated.")
            return None

    generate_emotional_btn.click(
        generate_emotional_speech,
        inputs=[
            regular_audio,
            regular_ref_text,
            gen_text_input_emotional,
        ] + speech_type_names + speech_type_audios + speech_type_ref_texts + [
            model_choice_emotional,
            remove_silence_emotional,
        ],
        outputs=audio_output_emotional,
    )

    def validate_speech_types(
        gen_text,
        regular_name,
        *args
    ):
        num_additional_speech_types = max_speech_types - 1
        speech_type_names_list = args[:num_additional_speech_types]

        speech_types_available = set()
        if regular_name:
            speech_types_available.add(regular_name)
        for name_input in speech_type_names_list:
            if name_input:
                speech_types_available.add(name_input)

        segments = parse_emotional_text(gen_text)
        speech_types_in_text = set(segment['emotion'] for segment in segments)

        missing_speech_types = speech_types_in_text - speech_types_available

        if missing_speech_types:
            return gr.update(interactive=False)
        else:
            return gr.update(interactive=True)

    gen_text_input_emotional.change(
        validate_speech_types,
        inputs=[gen_text_input_emotional, regular_name] + speech_type_names,
        outputs=generate_emotional_btn
    )

with gr.Blocks() as app:
    gr.Markdown(
        """
# F5 TTS (CPU Basic)

This is a CPU-optimized web UI for F5 TTS with advanced batch processing support. This app supports the following TTS models:

* [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching)
* [E2 TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS)

The checkpoint was trained with Polish British English American English German Russian Ukrainian other languages may not work correctly.

If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 10s, and shortening your prompt.

**NOTE: This runs on CPU Basic. Generation will be slower than GPU. Reference text will be automatically transcribed with Whisper if not provided. For best results, keep your reference clips short (<15s). Ensure the audio is fully uploaded before generating.**
"""
    )
    gr.HTML(
        """
        <a href="https://www.ko-fi.com/gregs40829" target="_blank">
            <img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;">
        </a>
        """
    )
    gr.TabbedInterface([app_tts, app_emotional, app_credits], ["TTS", "Multi-Style", "Credits"])

import click

@click.command()
@click.option("--port", "-p", default=None, type=int, help="Port to run the app on")
@click.option("--host", "-H", default=None, help="Host to run the app on")
@click.option(
    "--share",
    "-s",
    default=False,
    is_flag=True,
    help="Share the app via Gradio share link",
)
@click.option("--api", "-a", default=True, is_flag=True, help="Allow API access")
def main(port, host, share, api):
    global app
    print(f"Starting app on CPU...")
    app.queue(api_open=api).launch(
        server_name=host, server_port=port, share=share
    )


if __name__ == "__main__":
    main()