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| import spaces | |
| import gradio as gr | |
| import torch | |
| from transformers.models.speecht5.number_normalizer import EnglishNumberNormalizer | |
| from string import punctuation | |
| import re | |
| 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-expresso" | |
| 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 = "*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*." | |
| default_description = "Thomas speaks with emphasis and excitement at a moderate pace with high quality." | |
| 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.", | |
| "Thomas speaks in a sad tone at a moderate pace with high quality." | |
| ], | |
| [ | |
| "Did you know? You can reproduce this entire training recipe by following the steps outlined on the model card!", | |
| "Talia speaks quickly with excitement and high quality audio.", | |
| ], | |
| [ | |
| "But that's no secret! The entire project is open source first, with all release artefacts on the Hub.", | |
| "Elisabeth speaks happily at a slightly slower than average pace with high quality audio.", | |
| ], | |
| [ | |
| "Hey there! I'm Jerry. Or at least I think I am? I just need to check that quickly.", | |
| "Jerry speaks in a confused tone at a moderately slow pace with high quality audio.", | |
| ], | |
| [ | |
| "<laugh> It can even laugh! Do you believe it ? I don't!", | |
| "Talia speaks with laughter with high quality.", | |
| ], | |
| ] | |
| number_normalizer = EnglishNumberNormalizer() | |
| def preprocess(text): | |
| text = number_normalizer(text).strip() | |
| if text[-1] not in punctuation: | |
| text = f"{text}." | |
| abbreviations_pattern = r'\b[A-Z][A-Z\.]+\b' | |
| def separate_abb(chunk): | |
| chunk = chunk.replace(".", "") | |
| print(chunk) | |
| return " ".join(chunk) | |
| abbreviations = re.findall(abbreviations_pattern, text) | |
| for abv in abbreviations: | |
| if abv in text: | |
| text = text.replace(abv, separate_abb(abv)) | |
| return text | |
| def gen_tts(text, description): | |
| inputs = tokenizer(description, return_tensors="pt").to(device) | |
| prompt = tokenizer(preprocess(text), return_tensors="pt").to(device) | |
| set_seed(SEED) | |
| generation = model.generate(input_ids=inputs.input_ids, prompt_input_ids=prompt.input_ids) | |
| 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: Expresso ☕️️ | |
| </h1> | |
| </div> | |
| </div> | |
| """ | |
| ) | |
| gr.HTML( | |
| f""" | |
| <p><a href="https://huggingface.co/parler-tts/parler-tts-mini-expresso"> Parler-TTS Mini: Expresso</a> | |
| is a text-to-speech (TTS) model fine-tuned on the <a href="https://huggingface.co/datasets/ylacombe/expresso"> Expresso dataset</a>. | |
| It generates high-quality speech in a given <b>emotion</b> and <b>voice</b> that can be controlled through a simple text prompt.</p> | |
| <p>Tips for ensuring good generation: | |
| <ul> | |
| <li>Specify the name of a male speaker (Jerry, Thomas) or female speaker (Talia, Elisabeth) for consistent voices</li> | |
| <li>The model can generate in a range of emotions, including: "happy", "confused", "default" (meaning no particular emotion conveyed), "laughing", "sad", "whisper", "emphasis"</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>To emphasise particular words, wrap them in asterisk (e.g. *you* in the example above) and include "emphasis" in 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=default_description, 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. 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) |