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import spaces | |
import gradio as gr | |
import torch | |
from parler_tts import ParlerTTSForConditionalGeneration | |
from transformers import AutoTokenizer, AutoFeatureExtractor, set_seed | |
device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
repo_id = "parler-tts/parler_tts_mini_v0.1" | |
model = ParlerTTSForConditionalGeneration.from_pretrained(repo_id).to(device) | |
tokenizer = AutoTokenizer.from_pretrained(repo_id) | |
feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id) | |
SAMPLE_RATE = feature_extractor.sampling_rate | |
SEED = 42 | |
default_text = "Please surprise me and speak in whatever voice you enjoy." | |
examples = [ | |
[ | |
"Remember - this is only the first iteration of the model! To improve the prosody and naturalness of the speech further, we're scaling up the amount of training data by a factor of five times.", | |
"A male speaker with a low-pitched voice delivering his words at a fast pace in a small, confined space with a very clear audio and an animated tone." | |
], | |
[ | |
"'This is the best time of my life, Bartley,' she said happily.", | |
"A female speaker with a slightly low-pitched, quite monotone voice delivers her words at a slightly faster-than-average pace in a confined space with very clear audio.", | |
], | |
[ | |
"Montrose also, after having experienced still more variety of good and bad fortune, threw down his arms, and retired out of the kingdom.", | |
"A male speaker with a slightly high-pitched voice delivering his words at a slightly slow pace in a small, confined space with a touch of background noise and a quite monotone tone.", | |
], | |
[ | |
"Montrose also, after having experienced still more variety of good and bad fortune, threw down his arms, and retired out of the kingdom.", | |
"A male speaker with a low-pitched voice delivers his words at a fast pace and an animated tone, in a very spacious environment, accompanied by noticeable background noise.", | |
], | |
] | |
def gen_tts(text, description): | |
inputs = tokenizer(description, return_tensors="pt").to(device) | |
prompt = tokenizer(text, return_tensors="pt").to(device) | |
set_seed(SEED) | |
generation = model.generate( | |
input_ids=inputs.input_ids, prompt_input_ids=prompt.input_ids, do_sample=True, temperature=1.0 | |
) | |
audio_arr = generation.cpu().numpy().squeeze() | |
return SAMPLE_RATE, audio_arr | |
css = """ | |
#share-btn-container { | |
display: flex; | |
padding-left: 0.5rem !important; | |
padding-right: 0.5rem !important; | |
background-color: #000000; | |
justify-content: center; | |
align-items: center; | |
border-radius: 9999px !important; | |
width: 13rem; | |
margin-top: 10px; | |
margin-left: auto; | |
flex: unset !important; | |
} | |
#share-btn { | |
all: initial; | |
color: #ffffff; | |
font-weight: 600; | |
cursor: pointer; | |
font-family: 'IBM Plex Sans', sans-serif; | |
margin-left: 0.5rem !important; | |
padding-top: 0.25rem !important; | |
padding-bottom: 0.25rem !important; | |
right:0; | |
} | |
#share-btn * { | |
all: unset !important; | |
} | |
#share-btn-container div:nth-child(-n+2){ | |
width: auto !important; | |
min-height: 0px !important; | |
} | |
#share-btn-container .wrap { | |
display: none !important; | |
} | |
""" | |
with gr.Blocks(css=css) as block: | |
gr.HTML( | |
""" | |
<div style="text-align: center; max-width: 700px; margin: 0 auto;"> | |
<div | |
style=" | |
display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem; | |
" | |
> | |
<h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;"> | |
Parler-TTS 🗣️ | |
</h1> | |
</div> | |
</div> | |
""" | |
) | |
gr.HTML( | |
f""" | |
<p><a href="https://github.com/huggingface/parler-tts"> Parler-TTS</a> is a training and inference library for | |
high-fidelity text-to-speech (TTS) models. The model demonstrated here, <a href="https://huggingface.co/parler-tts/parler_tts_mini_v0.1"> Parler-TTS Mini v0.1</a>, | |
is the first iteration model trained using 10k hours of narrated audiobooks. It generates high-quality speech | |
with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).</p> | |
<p>Tips for ensuring good generation: | |
<ul> | |
<li>Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise</li> | |
<li>Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech</li> | |
<li>The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt</li> | |
</ul> | |
</p> | |
""" | |
) | |
with gr.Row(): | |
with gr.Column(): | |
input_text = gr.Textbox(label="Input Text", lines=2, value=default_text, elem_id="input_text") | |
description = gr.Textbox(label="Description", lines=2, value="", elem_id="input_description") | |
run_button = gr.Button("Generate Audio", variant="primary") | |
with gr.Column(): | |
audio_out = gr.Audio(label="Parler-TTS generation", type="numpy", elem_id="audio_out") | |
inputs = [input_text, description] | |
outputs = [audio_out] | |
gr.Examples(examples=examples, fn=gen_tts, inputs=inputs, outputs=outputs, cache_examples=True) | |
run_button.click(fn=gen_tts, inputs=inputs, outputs=outputs, queue=True) | |
gr.HTML( | |
""" | |
<p>To improve the prosody and naturalness of the speech further, we're scaling up the amount of training data to 50k hours of speech. | |
The v1 release of the model will be trained on this data, as well as inference optimisations, such as flash attention | |
and torch compile, that will improve the latency by 2-4x. If you want to find out more about how this model was trained and even fine-tune it yourself, check-out the | |
<a href="https://github.com/huggingface/parler-tts"> Parler-TTS</a> repository on GitHub.</p> | |
<p>The Parler-TTS codebase and its associated checkpoints are licensed under <a href='https://github.com/huggingface/parler-tts?tab=Apache-2.0-1-ov-file#readme'> Apache 2.0</a></p>. | |
""" | |
) | |
block.queue() | |
block.launch(share=True) | |