app.py

from huggingface_hub import snapshot_download
from katsu import Katsu
from models import build_model
import gradio as gr
import numpy as np
import os
import phonemizer
import pypdf
import random
import re
import spaces
import torch
import yaml

device = 'cuda' if torch.cuda.is_available() else 'cpu'

snapshot = snapshot_download(repo_id='hexgrad/kokoro', allow_patterns=['*.pt', '*.pth', '*.yml'], use_auth_token=os.environ['TOKEN'])
config = yaml.safe_load(open(os.path.join(snapshot, 'config.yml')))
model = build_model(config['model_params'])
_ = [model[key].eval() for key in model]
_ = [model[key].to(device) for key in model]
for key, state_dict in torch.load(os.path.join(snapshot, 'net.pth'), map_location='cpu', weights_only=True)['net'].items():
    assert key in model, key
    try:
        model[key].load_state_dict(state_dict)
    except:
        state_dict = {k[7:]: v for k, v in state_dict.items()}
        model[key].load_state_dict(state_dict, strict=False)

PARAM_COUNT = sum(p.numel() for value in model.values() for p in value.parameters())
assert PARAM_COUNT < 82_000_000, PARAM_COUNT

random_texts = {}
for lang in ['en', 'ja']:
    with open(f'{lang}.txt', 'r') as r:
        random_texts[lang] = [line.strip() for line in r]

def get_random_text(voice):
    if voice[0] == 'j':
        lang = 'ja'
    else:
        lang = 'en'
    return random.choice(random_texts[lang])

def parens_to_angles(s):
    return s.replace('(', '«').replace(')', '»')

def split_num(num):
    if '.' not in num:
        a, b = num.split('.')
        b = ' '.join(b)
        return f'{a} point {b}'
    assert num.isdigit() and len(num) == 4, num
    year = int(num)
    if year < 1100 or year % 1000 < 10:
        return num
    first_half = num[:2]
    second_half = num[2:]
    second_half_int = int(second_half)
    if 100 <= year % 1000 <= 999:
        if second_half == '00':
            return f'{first_half} hundred'
        elif second_half_int < 10:
            return f'{first_half} oh {second_half_int}'
    return ' '.join([first_half, second_half])

def normalize(text):
    # TODO: Custom text normalization rules?
    text = re.sub(r'\bD[Rr]\.(?= [A-Z])', 'Doctor', text)
    text = re.sub(r'\b(?:Mr\.|MR\.(?= [A-Z]))', 'Mister', text)
    text = re.sub(r'\b(?:Ms\.|MS\.(?= [A-Z]))', 'Miss', text)
    text = re.sub(r'\b(?:Mrs\.|MRS\.(?= [A-Z]))', 'Mrs', text)
    text = re.sub(r'\betc\.(?! [A-Z])', 'etc', text)
    text = re.sub(r'\b([Yy])eah\b', r"\1e'a", text)
    text = text.replace(chr(8216), "'").replace(chr(8217), "'")
    text = text.replace(chr(8220), '"').replace(chr(8221), '"')
    text = re.sub(r'[^\S \n]', ' ', text)
    text = re.sub(r'  +', ' ', text)
    text = re.sub(r'(?<=\n) +(?=\n)', '', text)
    text = re.sub(r'\d*\.\d+|\b\d{4}\b', split_num, text)
    text = re.sub(r'(?<=\d),(?=\d)', '', text)
    text = re.sub(r'(?<=\d)-(?=\d)', ' to ', text) # TODO: could be minus
    text = re.sub(r'(?<=\d):(?=\d)', ' ', text)
    return parens_to_angles(text).strip()

phonemizers = dict(
    a=phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True, with_stress=True),
    b=phonemizer.backend.EspeakBackend(language='en-gb', preserve_punctuation=True, with_stress=True),
    j=Katsu(),
)

def phonemize(text, voice, norm=True):
    lang = voice[0]
    if norm:
        text = normalize(text)
    ps = phonemizers[lang].phonemize([text])
    ps = ps[0] if ps else ''
    # TODO: Custom phonemization rules?
    ps = parens_to_angles(ps)
    # https://en.wiktionary.org/wiki/kokoro#English
    ps = ps.replace('kəkˈoːɹoʊ', 'kˈoʊkəɹoʊ').replace('kəkˈɔːɹəʊ', 'kˈəʊkəɹəʊ')
    ps = ''.join(filter(lambda p: p in VOCAB, ps))
    if lang == 'j' and any(p in 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' for p in ps):
        gr.Warning('Japanese tokenizer does not handle English letters.')
    return ps.strip()

def length_to_mask(lengths):
    mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
    mask = torch.gt(mask+1, lengths.unsqueeze(1))
    return mask

def get_vocab():
    _pad = "$"
    _punctuation = ';:,.!?¡¿—…"«»“” '
    _letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
    _letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
    symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
    dicts = {}
    for i in range(len((symbols))):
        dicts[symbols[i]] = i
    return dicts

VOCAB = get_vocab()

def tokenize(ps):
    return [i for i in map(VOCAB.get, ps) if i is not None]

# ⭐ Starred voices are more stable. 🧪 Experimental voices are less stable.
CHOICES = {
'🇺🇸 🚺 American Female ⭐': 'af',
'🇺🇸 🚺 Bella': 'af_bella',
'🇺🇸 🚺 Nicole': 'af_nicole',
'🇺🇸 🚺 Sarah': 'af_sarah',
'🇺🇸 🚺 Sky 🧪': 'af_sky',
'🇺🇸 🚹 Adam 🧪': 'am_adam',
'🇺🇸 🚹 Michael': 'am_michael',
'🇬🇧 🚹 Lewis 🧪': 'bm_lewis',
'🇯🇵 🚺 Japanese Female': 'jf_0',
}
VOICES = {k: torch.load(os.path.join(snapshot, 'voicepacks', f'{k}.pt'), weights_only=True).to(device) for k in CHOICES.values()}

np_log_99 = np.log(99)
def s_curve(p):
    if p <= 0:
        return 0
    elif p >= 1:
        return 1
    s = 1 / (1 + np.exp((1-p*2)*np_log_99))
    s = (s-0.01) * 50/49
    return s

SAMPLE_RATE = 24000

@spaces.GPU(duration=10)
@torch.no_grad()
def forward(tokens, voice, speed):
    ref_s = VOICES[voice][len(tokens)]
    tokens = torch.LongTensor([[0, *tokens, 0]]).to(device)
    input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
    text_mask = length_to_mask(input_lengths).to(device)
    bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
    d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
    s = ref_s[:, 128:]
    d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask)
    x, _ = model.predictor.lstm(d)
    duration = model.predictor.duration_proj(x)
    duration = torch.sigmoid(duration).sum(axis=-1) / speed
    pred_dur = torch.round(duration).clamp(min=1).long()
    pred_aln_trg = torch.zeros(input_lengths, pred_dur.sum().item())
    c_frame = 0
    for i in range(pred_aln_trg.size(0)):
        pred_aln_trg[i, c_frame:c_frame + pred_dur[0,i].item()] = 1
        c_frame += pred_dur[0,i].item()
    en = d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device)
    F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
    t_en = model.text_encoder(tokens, input_lengths, text_mask)
    asr = t_en @ pred_aln_trg.unsqueeze(0).to(device)
    return model.decoder(asr, F0_pred, N_pred, ref_s[:, :128]).squeeze().cpu().numpy()

def generate(text, voice, ps=None, speed=1.0, opening_cut=4000, closing_cut=2000, ease_in=3000, ease_out=1000, pad_before=0, pad_after=0):
    if voice not in VOICES:
        # Ensure stability for https://huggingface.co./spaces/Pendrokar/TTS-Spaces-Arena
        voice = 'af'
    ps = ps or phonemize(text, voice)
    tokens = tokenize(ps)
    if not tokens:
        return (None, '')
    elif len(tokens) > 510:
        tokens = tokens[:510]
    ps = ''.join(next(k for k, v in VOCAB.items() if i == v) for i in tokens)
    try:
        out = forward(tokens, voice, speed)
    except gr.exceptions.Error as e:
        raise gr.Error(e)
        return (None, '')
    opening_cut = int(opening_cut / speed)
    if opening_cut > 0:
        out = out[opening_cut:]
    closing_cut = int(closing_cut / speed)
    if closing_cut > 0:
        out = out[:-closing_cut]
    ease_in = min(int(ease_in / speed), len(out)//2)
    for i in range(ease_in):
        out[i] *= s_curve(i / ease_in)
    ease_out = min(int(ease_out / speed), len(out)//2)
    for i in range(ease_out):
        out[-i-1] *= s_curve(i / ease_out)
    pad_before = int(pad_before / speed)
    if pad_before > 0:
        out = np.concatenate([np.zeros(pad_before), out])
    pad_after = int(pad_after / speed)
    if pad_after > 0:
        out = np.concatenate([out, np.zeros(pad_after)])
    return ((SAMPLE_RATE, out), ps)

def toggle_autoplay(autoplay):
    return gr.Audio(interactive=False, label='Output Audio', autoplay=autoplay)

with gr.Blocks() as basic_tts:
    with gr.Row():
        gr.Markdown('Generate speech for one segment of text (up to 510 tokens) using Kokoro, a TTS model with 80 million parameters.')
    with gr.Row():
        with gr.Column():
            text = gr.Textbox(label='Input Text')
            voice = gr.Dropdown(list(CHOICES.items()), label='Voice', info='⭐ Starred voices are more stable. 🧪 Experimental voices are less stable.')
            with gr.Row():
                random_btn = gr.Button('Random Text', variant='secondary')
                generate_btn = gr.Button('Generate', variant='primary')
            random_btn.click(get_random_text, inputs=[voice], outputs=[text])
            with gr.Accordion('Input Tokens', open=False):
                in_ps = gr.Textbox(show_label=False, info='Override the input text with custom phonemes. Leave this blank to automatically tokenize the input text instead.')
                with gr.Row():
                    clear_btn = gr.ClearButton(in_ps)
                    phonemize_btn = gr.Button('Tokenize Input Text', variant='primary')
            phonemize_btn.click(phonemize, inputs=[text, voice], outputs=[in_ps])
        with gr.Column():
            audio = gr.Audio(interactive=False, label='Output Audio', autoplay=True)
            with gr.Accordion('Output Tokens', open=True):
                out_ps = gr.Textbox(interactive=False, show_label=False, info='Tokens used to generate the audio, up to 510 allowed. Same as input tokens if supplied, excluding unknowns.')
    with gr.Accordion('Audio Settings', open=False):
        with gr.Row():
            autoplay = gr.Checkbox(value=True, label='Autoplay')
        with gr.Row():
            speed = gr.Slider(minimum=0.5, maximum=2.0, value=1.0, step=0.1, label='Speed', info='⚡️ Adjust the speed of the audio. The settings below are auto-scaled by speed.')
        with gr.Row():
            with gr.Column():
                opening_cut = gr.Slider(minimum=0, maximum=24000, value=4000, step=1000, label='Opening Cut', info='✂️ Cut this many samples from the start.')
            with gr.Column():
                closing_cut = gr.Slider(minimum=0, maximum=24000, value=2000, step=1000, label='Closing Cut', info='✂️ Cut this many samples from the end.')
        with gr.Row():
            with gr.Column():
                ease_in = gr.Slider(minimum=0, maximum=24000, value=3000, step=1000, label='Ease In', info='🚀 Ease in for this many samples, after opening cut.')
            with gr.Column():
                ease_out = gr.Slider(minimum=0, maximum=24000, value=1000, step=1000, label='Ease Out', info='📐 Ease out for this many samples, before closing cut.')
        with gr.Row():
            with gr.Column():
                pad_before = gr.Slider(minimum=0, maximum=24000, value=0, step=1000, label='Pad Before', info='🔇 How many samples of silence to insert before the start.')
            with gr.Column():
                pad_after = gr.Slider(minimum=0, maximum=24000, value=0, step=1000, label='Pad After', info='🔇 How many samples of silence to append after the end.')
    autoplay.change(toggle_autoplay, inputs=[autoplay], outputs=[audio])
    text.submit(generate, inputs=[text, voice, in_ps, speed, opening_cut, closing_cut, ease_in, ease_out, pad_before, pad_after], outputs=[audio, out_ps])
    generate_btn.click(generate, inputs=[text, voice, in_ps, speed, opening_cut, closing_cut, ease_in, ease_out, pad_before, pad_after], outputs=[audio, out_ps])

@spaces.GPU
@torch.no_grad()
def lf_forward(token_lists, voice, speed):
    voicepack = VOICES[voice]
    outs = []
    for tokens in token_lists:
        ref_s = voicepack[len(tokens)]
        s = ref_s[:, 128:]
        tokens = torch.LongTensor([[0, *tokens, 0]]).to(device)
        input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
        text_mask = length_to_mask(input_lengths).to(device)
        bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
        d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
        d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask)
        x, _ = model.predictor.lstm(d)
        duration = model.predictor.duration_proj(x)
        duration = torch.sigmoid(duration).sum(axis=-1) / speed
        pred_dur = torch.round(duration).clamp(min=1).long()
        pred_aln_trg = torch.zeros(input_lengths, pred_dur.sum().item())
        c_frame = 0
        for i in range(pred_aln_trg.size(0)):
            pred_aln_trg[i, c_frame:c_frame + pred_dur[0,i].item()] = 1
            c_frame += pred_dur[0,i].item()
        en = d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device)
        F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
        t_en = model.text_encoder(tokens, input_lengths, text_mask)
        asr = t_en @ pred_aln_trg.unsqueeze(0).to(device)
        outs.append(model.decoder(asr, F0_pred, N_pred, ref_s[:, :128]).squeeze().cpu().numpy())
    return outs

def resplit_strings(arr):
    # Handle edge cases
    if not arr:
        return '', ''
    if len(arr) == 1:
        return arr[0], ''
    # Try each possible split point
    min_diff = float('inf')
    best_split = 0
    # Calculate lengths when joined with spaces
    lengths = [len(s) for s in arr]
    spaces = len(arr) - 1  # Total spaces needed
    # Try each split point
    left_len = 0
    right_len = sum(lengths) + spaces
    for i in range(1, len(arr)):
        # Add current word and space to left side
        left_len += lengths[i-1] + (1 if i > 1 else 0)
        # Remove current word and space from right side
        right_len -= lengths[i-1] + 1
        diff = abs(left_len - right_len)
        if diff < min_diff:
            min_diff = diff
            best_split = i
    # Join the strings with the best split point
    return ' '.join(arr[:best_split]), ' '.join(arr[best_split:])

def recursive_split(text, voice):
    if not text:
        return []
    tokens = phonemize(text, voice, norm=False)
    if len(tokens) < 511:
        return [(text, tokens, len(tokens))] if tokens else []
    if ' ' not in text:
        return []
    for punctuation in ['!.?…', ':;', ',—']:
        splits = re.split(f'(?:(?<=[{punctuation}])|(?<=[{punctuation}]["\'»])|(?<=[{punctuation}]["\'»]["\'»])) ', text)
        if len(splits) > 1:
            break
        else:
            splits = None
    splits = splits or text.split(' ')
    a, b = resplit_strings(splits)
    return recursive_split(a, voice) + recursive_split(b, voice)

def segment_and_tokenize(text, voice, skip_square_brackets=True, newline_split=2):
    if skip_square_brackets:
        text = re.sub(r'\[.*?\]', '', text)
    texts = [t.strip() for t in re.split('\n{'+str(newline_split)+',}', normalize(text))] if newline_split > 0 else [normalize(text)]
    segments = [row for t in texts for row in recursive_split(t, voice)]
    return [(i, *row) for i, row in enumerate(segments)]

def lf_generate(segments, voice, speed=1.0, opening_cut=4000, closing_cut=2000, ease_in=3000, ease_out=1000, pad_before=5000, pad_after=5000, pad_between=10000):
    token_lists = list(map(tokenize, segments['Tokens']))
    wavs = []
    opening_cut = int(opening_cut / speed)
    closing_cut = int(closing_cut / speed)
    pad_between = int(pad_between / speed)
    batch_size = 100
    for i in range(0, len(token_lists), batch_size):
        try:
            outs = lf_forward(token_lists[i:i+batch_size], voice, speed)
        except gr.exceptions.Error as e:
            if wavs:
                gr.Warning(str(e))
            else:
                raise gr.Error(e)
            break
        for out in outs:
            if opening_cut > 0:
                out = out[opening_cut:]
            if closing_cut > 0:
                out = out[:-closing_cut]
            ease_in = min(int(ease_in / speed), len(out)//2)
            for i in range(ease_in):
                out[i] *= s_curve(i / ease_in)
            ease_out = min(int(ease_out / speed), len(out)//2)
            for i in range(ease_out):
                out[-i-1] *= s_curve(i / ease_out)
            if wavs and pad_between > 0:
                wavs.append(np.zeros(pad_between))
            wavs.append(out)
    pad_before = int(pad_before / speed)
    if pad_before > 0:
        wavs.insert(0, np.zeros(pad_before))
    pad_after = int(pad_after / speed)
    if pad_after > 0:
        wavs.append(np.zeros(pad_after))
    return (SAMPLE_RATE, np.concatenate(wavs)) if wavs else None

def did_change_segments(segments):
    x = len(segments) if segments['Length'].any() else 0
    return [
        gr.Button('Tokenize', variant='secondary' if x else 'primary'),
        gr.Button(f'Generate x{x}', variant='primary' if x else 'secondary', interactive=x > 0),
    ]

def extract_text(file):
    if file.endswith('.pdf'):
        with open(file, 'rb') as rb:
            pdf_reader = pypdf.PdfReader(rb)
            return '\n'.join([page.extract_text() for page in pdf_reader.pages])
    elif file.endswith('.txt'):
        with open(file, 'r') as r:
            return '\n'.join([line for line in r])
    return None

with gr.Blocks() as lf_tts:
    with gr.Row():
        gr.Markdown('Generate speech in batches of 100 text segments and automatically join them together. This may exhaust your ZeroGPU quota.')
    with gr.Row():
        with gr.Column():
            file_input = gr.File(file_types=['.pdf', '.txt'], label='Input File: pdf or txt')
            text = gr.Textbox(label='Input Text')
            file_input.upload(fn=extract_text, inputs=[file_input], outputs=[text])
            voice = gr.Dropdown(list(CHOICES.items()), label='Voice', info='⭐ Starred voices are more stable. 🧪 Experimental voices are less stable.')
            with gr.Accordion('Text Settings', open=False):
                skip_square_brackets = gr.Checkbox(True, label='Skip [Square Brackets]', info='Recommended for academic papers, Wikipedia articles, or texts with citations.')
                newline_split = gr.Number(2, label='Newline Split', info='Split the input text on this many newlines. Affects how the text is segmented.', precision=0, minimum=0)
            with gr.Row():
                segment_btn = gr.Button('Tokenize', variant='primary')
                generate_btn = gr.Button('Generate x0', variant='secondary', interactive=False)
        with gr.Column():
            audio = gr.Audio(interactive=False, label='Output Audio')
            with gr.Accordion('Audio Settings', open=False):
                with gr.Row():
                    speed = gr.Slider(minimum=0.5, maximum=2.0, value=1.0, step=0.1, label='Speed', info='⚡️ Adjust the speed of the audio. The settings below are auto-scaled by speed.')
                with gr.Row():
                    with gr.Column():
                        opening_cut = gr.Slider(minimum=0, maximum=24000, value=4000, step=1000, label='Opening Cut', info='✂️ Cut this many samples from the start.')
                    with gr.Column():
                        closing_cut = gr.Slider(minimum=0, maximum=24000, value=2000, step=1000, label='Closing Cut', info='✂️ Cut this many samples from the end.')
                with gr.Row():
                    with gr.Column():
                        ease_in = gr.Slider(minimum=0, maximum=24000, value=3000, step=1000, label='Ease In', info='🚀 Ease in for this many samples, after opening cut.')
                    with gr.Column():
                        ease_out = gr.Slider(minimum=0, maximum=24000, value=1000, step=1000, label='Ease Out', info='📐 Ease out for this many samples, before closing cut.')
                with gr.Row():
                    with gr.Column():
                        pad_before = gr.Slider(minimum=0, maximum=24000, value=5000, step=1000, label='Pad Before', info='🔇 How many samples of silence to insert before the start.')
                    with gr.Column():
                        pad_after = gr.Slider(minimum=0, maximum=24000, value=5000, step=1000, label='Pad After', info='🔇 How many samples of silence to append after the end.')
                with gr.Row():
                    pad_between = gr.Slider(minimum=0, maximum=24000, value=10000, step=1000, label='Pad Between', info='🔇 How many samples of silence to insert between segments.')
    with gr.Row():
        segments = gr.Dataframe(headers=['#', 'Text', 'Tokens', 'Length'], row_count=(1, 'dynamic'), col_count=(4, 'fixed'), label='Segments', interactive=False, wrap=True)
        segments.change(fn=did_change_segments, inputs=[segments], outputs=[segment_btn, generate_btn])
    segment_btn.click(segment_and_tokenize, inputs=[text, voice, skip_square_brackets, newline_split], outputs=[segments])
    generate_btn.click(lf_generate, inputs=[segments, voice, speed, opening_cut, closing_cut, ease_in, ease_out, pad_before, pad_after, pad_between], outputs=[audio])

with gr.Blocks() as about:
    gr.Markdown("""
Kokoro is a frontier TTS model for its size. It has 80 million parameters,<sup>[1]</sup> uses a lean StyleTTS 2 architecture,<sup>[2]</sup> and was trained on high-quality data.

The weights are currently private, but a free public demo is hosted here, at `https://hf.co/spaces/hexgrad/Kokoro-TTS`

### Will this be open sourced?
There currently isn't a release date scheduled for the weights. The inference code in this space is MIT licensed. The architecture was already published by Li et al, with MIT licensed code and pretrained weights.<sup>[2]</sup>

### What does it mean if a voice is unstable?
An unstable voice is more likely to stumble or produce unnatural artifacts, especially on short or strange texts.

### Compute
The model was trained on 1x A100-class 80GB instances rented from [Vast.ai](https://cloud.vast.ai/?ref_id=79907).<sup>[3]</sup><br/>
Vast was chosen over other compute providers due to its competitive on-demand hourly rates.<br/>
The average hourly cost for the 1x A100-class 80GB VRAM instances used for training was below $1/hr — around half the quoted rates from other providers.

### Licenses
Inference code: MIT<br/>
espeak-ng dependency: GPL-3.0<sup>[4]</sup><br/>
Random English texts: Unknown<sup>[5]</sup><br/>
Random Japanese texts: CC0 public domain<sup>[6]</sup>

### References
1. Kokoro parameter count | https://hf.co/spaces/hexgrad/Kokoro-TTS/blob/main/app.py#L31
2. StyleTTS 2 | https://github.com/yl4579/StyleTTS2
3. Vast.ai referral link | https://cloud.vast.ai/?ref_id=79907
4. eSpeak NG | https://github.com/espeak-ng/espeak-ng
5. Quotable Data | https://github.com/quotable-io/data/blob/master/data/quotes.json
6. Common Voice Japanese sentences | https://github.com/common-voice/common-voice/tree/main/server/data/ja

### Contact
@rzvzn on Discord
""")

with gr.Blocks() as api_info:
    gr.Markdown("""
This Space can be used via API. The following code block can be copied and run in one Google Colab cell.
```
# 1. Install the Gradio Python client
!pip install -q gradio_client

# 2. Initialize the client
from gradio_client import Client
client = Client('hexgrad/Kokoro-TTS')

# 3. Call the generate endpoint, which returns a pair: an audio path and a string of output phonemes
audio_path, out_ps = client.predict(
    text="How could I know? It's an unanswerable question. Like asking an unborn child if they'll lead a good life. They haven't even been born.",
    voice='af',
    api_name='/generate'
)

# 4. Display the audio and print the output phonemes
from IPython.display import display, Audio
display(Audio(audio_path))
print(out_ps)
```
Note that this Space and the underlying Kokoro model are both under development and subject to change. Reliability is not guaranteed. Hugging Face and/or Gradio might enforce their own rate limits.
""")

with gr.Blocks() as version_info:
    gr.Markdown("""
| Model Version | Date | Validation losses (mel/dur/f0) |
| ------- | ---- | ------------------------------ |
| v0.19 | 2024 Nov 22 | 0.261 / 0.627 / 1.897 |
| v0.16 | 2024 Nov 15 | 0.263 / 0.646 / 1.934 |
| v0.14 | 2024 Nov 12 | 0.262 / 0.642 / 1.889 |
""")

with gr.Blocks() as app:
    gr.TabbedInterface(
        [basic_tts, lf_tts, about, api_info, version_info],
        ['🗣️ Basic TTS', '📖 Long-Form', 'ℹ️ About', '🚀 Gradio API', '📝 Version History'],
    )

if __name__ == '__main__':
    app.queue(api_open=True).launch()