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| 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 | |
| # Set device to CPU only | |
| device = "cpu" | |
| # Load Mini model and associated components with low memory usage | |
| repo_id = "TArtx/parler-tts-mini-v1-finetuned-12" | |
| model = ParlerTTSForConditionalGeneration.from_pretrained(repo_id).to(device) | |
| tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1") | |
| feature_extractor = AutoFeatureExtractor.from_pretrained(parler-tts/parler-tts-mini-v1) | |
| # Constants | |
| SAMPLE_RATE = feature_extractor.sampling_rate | |
| SEED = 42 | |
| # Default input text and description | |
| default_text = "This is a demonstration of my ability to convert written words into spoken language, seamlessly and naturally. As a text-to-speech model, my goal is to sound as clear and engaging as a human, making sure every word I say leaves an impression." | |
| default_description = "moderate speed, very clear, monotone, wonderful speech quality" | |
| # Number normalizer | |
| number_normalizer = EnglishNumberNormalizer() | |
| # Preprocessing function | |
| def preprocess(text): | |
| text = number_normalizer(text).strip() | |
| text = text.replace("-", " ") | |
| 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(".", "") | |
| 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 | |
| # TTS generation function | |
| def gen_tts(text, description): | |
| try: | |
| # Tokenize inputs and prompts with truncation to avoid memory issues | |
| inputs = tokenizer(description.strip(), return_tensors="pt", truncation=True, max_length=128).to(device) | |
| prompt = tokenizer(preprocess(text), return_tensors="pt", truncation=True, max_length=128).to(device) | |
| set_seed(SEED) | |
| generation = model.generate( | |
| input_ids=inputs.input_ids, | |
| prompt_input_ids=prompt.input_ids, | |
| attention_mask=inputs.attention_mask, | |
| prompt_attention_mask=prompt.prompt_attention_mask, | |
| do_sample=True, | |
| temperature=1.0, | |
| ) | |
| audio_arr = generation.cpu().numpy().squeeze() | |
| return SAMPLE_RATE, audio_arr | |
| except Exception as e: | |
| return SAMPLE_RATE, f"Error: {str(e)}" | |
| # Gradio interface | |
| with gr.Blocks() as block: | |
| gr.Markdown( | |
| """ | |
| ## Parler-TTS 🗣️ | |
| Parler-TTS is a training and inference library for high-fidelity text-to-speech (TTS) models. This demo uses the Mini v1 model. | |
| """ | |
| ) | |
| 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] | |
| run_button.click(fn=gen_tts, inputs=inputs, outputs=outputs, queue=True) | |
| # Launch the interface | |
| block.queue() | |
| block.launch() |