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import spaces
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import gradio as gr
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import torch
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from transformers.models.speecht5.number_normalizer import EnglishNumberNormalizer
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from string import punctuation
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import re
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from parler_tts import ParlerTTSForConditionalGeneration
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from transformers import AutoTokenizer, AutoFeatureExtractor, set_seed
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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repo_id = "parler-tts/parler_tts_mini_v0.1"
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model = ParlerTTSForConditionalGeneration.from_pretrained(repo_id).to(device)
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tokenizer = AutoTokenizer.from_pretrained(repo_id)
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feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
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SAMPLE_RATE = feature_extractor.sampling_rate
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SEED = 42
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default_text = "Please surprise me and speak in whatever voice you enjoy."
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examples = [
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[
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"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.",
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"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."
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],
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[
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"'This is the best time of my life, Bartley,' she said happily.",
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"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.",
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],
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[
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"Montrose also, after having experienced still more variety of good and bad fortune, threw down his arms, and retired out of the kingdom.",
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"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.",
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],
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[
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"Montrose also, after having experienced still more variety of good and bad fortune, threw down his arms, and retired out of the kingdom.",
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"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.",
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],
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]
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number_normalizer = EnglishNumberNormalizer()
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def preprocess(text):
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text = number_normalizer(text).strip()
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text = text.replace("-", " ")
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if text[-1] not in punctuation:
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text = f"{text}."
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abbreviations_pattern = r'\b[A-Z][A-Z\.]+\b'
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def separate_abb(chunk):
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chunk = chunk.replace(".","")
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print(chunk)
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return " ".join(chunk)
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abbreviations = re.findall(abbreviations_pattern, text)
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for abv in abbreviations:
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if abv in text:
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text = text.replace(abv, separate_abb(abv))
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return text
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@spaces.GPU
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def gen_tts(text, description):
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inputs = tokenizer(description, return_tensors="pt").to(device)
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prompt = tokenizer(preprocess(text), return_tensors="pt").to(device)
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set_seed(SEED)
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generation = model.generate(
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input_ids=inputs.input_ids, prompt_input_ids=prompt.input_ids, do_sample=True, temperature=1.0
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)
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audio_arr = generation.cpu().numpy().squeeze()
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return SAMPLE_RATE, audio_arr
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css = """
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#share-btn-container {
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display: flex;
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padding-left: 0.5rem !important;
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padding-right: 0.5rem !important;
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background-color: #000000;
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justify-content: center;
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align-items: center;
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border-radius: 9999px !important;
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width: 13rem;
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margin-top: 10px;
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margin-left: auto;
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flex: unset !important;
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}
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#share-btn {
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all: initial;
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color: #ffffff;
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font-weight: 600;
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cursor: pointer;
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font-family: 'IBM Plex Sans', sans-serif;
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margin-left: 0.5rem !important;
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padding-top: 0.25rem !important;
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padding-bottom: 0.25rem !important;
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right:0;
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}
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#share-btn * {
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all: unset !important;
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}
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#share-btn-container div:nth-child(-n+2){
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width: auto !important;
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min-height: 0px !important;
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}
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#share-btn-container .wrap {
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display: none !important;
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}
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"""
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with gr.Blocks(css=css) as block:
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gr.HTML(
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"""
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<div style="text-align: center; max-width: 700px; margin: 0 auto;">
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<div
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style="
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display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem;
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"
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>
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<h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
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Parler-TTS 🗣️
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</h1>
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</div>
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</div>
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"""
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)
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gr.HTML(
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f"""
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<p><a href="https://github.com/huggingface/parler-tts"> Parler-TTS</a> is a training and inference library for
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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>,
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is the first iteration model trained using 10k hours of narrated audiobooks. It generates high-quality speech
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with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).</p>
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<p>Tips for ensuring good generation:
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<ul>
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<li>Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise</li>
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<li>Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech</li>
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<li>The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt</li>
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</ul>
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</p>
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"""
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)
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with gr.Row():
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with gr.Column():
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input_text = gr.Textbox(label="Input Text", lines=2, value=default_text, elem_id="input_text")
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description = gr.Textbox(label="Description", lines=2, value="", elem_id="input_description")
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run_button = gr.Button("Generate Audio", variant="primary")
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with gr.Column():
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audio_out = gr.Audio(label="Parler-TTS generation", type="numpy", elem_id="audio_out")
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inputs = [input_text, description]
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outputs = [audio_out]
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gr.Examples(examples=examples, fn=gen_tts, inputs=inputs, outputs=outputs, cache_examples=True)
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run_button.click(fn=gen_tts, inputs=inputs, outputs=outputs, queue=True)
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gr.HTML(
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"""
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<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.
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The v1 release of the model will be trained on this data, as well as inference optimisations, such as flash attention
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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
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<a href="https://github.com/huggingface/parler-tts"> Parler-TTS</a> repository on GitHub.</p>
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<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>
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"""
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)
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block.queue()
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block.launch(share=True) |
