app.py

import gradio as gr

# def greet(name):
#     return "Hello " + name + "!!"

# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()

import spaces
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, pipeline, set_seed

device = "cuda:0" if torch.cuda.is_available() else "cpu"

repo_id = "j2moreno/test-model/saved_model"
s
model = AutoModelForCausalLM.from_pretrained(repo_id).to(device)
tokenizer = AutoTokenizer.from_pretrained(repo_id)
# feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)

SEED = 42

default_text = "Ask me about Leonardo Moreno"
# 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 preprocess(text):
#     text = number_normalizer(text).strip()
#     text = text.replace("-", " ")
#     if text[-1] not in punctuation:
#         text = f"{text}."s
    
#     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

@spaces.GPU
def generate_response(text):
    set_seed(SEED)

    tokenized_prompt = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
    # print(tokenized_prompt)

    output_sequences = model.generate(**tokenized_prompt, max_length=1024, num_return_sequences=1)
    decoded_output = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
    # print(decoded_output)

    return decoded_output

iface = gr.Interface(fn=generate_response, inputs="text", outputs="text")
iface.launch()

# 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)