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| 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 | |
| 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) |