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_300M_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 = 41 default_text = "Please surprise me and speak in whatever voice you enjoy." title = "# Parler-TTS " examples = [ [ "'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 delivering his words at a fast pace in a small, confined space with a lot of background noise and an animated tone.", ], ] 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.Markdown(title) 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) block.queue() block.launch(share=True)