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import gradio as gr |
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import torch |
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import os |
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from io import BytesIO |
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import base64 |
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import numpy as np |
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from pydub import AudioSegment |
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from parler_tts import ParlerTTSForConditionalGeneration |
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from transformers import AutoTokenizer, AutoFeatureExtractor, set_seed |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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repo_id = "parler-tts/parler_tts_mini_v0.1" |
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model = ParlerTTSForConditionalGeneration.from_pretrained(repo_id).to(device) |
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tokenizer = AutoTokenizer.from_pretrained(repo_id) |
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feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id) |
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SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret') |
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SAMPLE_RATE = feature_extractor.sampling_rate |
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SEED = 42 |
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def gen_tts(secret_token, text, description): |
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if secret_token != SECRET_TOKEN: |
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raise gr.Error( |
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f'Invalid secret token. Please fork the original space if you want to use it for yourself.') |
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inputs = tokenizer(description, return_tensors="pt").to(device) |
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prompt = tokenizer(text, return_tensors="pt").to(device) |
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set_seed(SEED) |
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generation = model.generate( |
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input_ids=inputs.input_ids, prompt_input_ids=prompt.input_ids, do_sample=True, temperature=1.0 |
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) |
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audio_arr = generation.cpu().numpy().squeeze() |
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samples = np.array(audio_arr * (2**15 - 1), dtype=np.int16) |
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sound = AudioSegment( |
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samples.tobytes(), |
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frame_rate=SAMPLE_RATE, |
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sample_width=samples.dtype.itemsize, |
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channels=1 |
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) |
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buff_mp3 = BytesIO() |
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sound.export(buff_mp3, format="mp3") |
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buff_mp3.seek(0) |
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audio_base64 = base64.b64encode(buff_mp3.read()).decode('utf-8') |
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data_uri = 'data:audio/mp3;base64,' + audio_base64 |
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return data_uri |
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with gr.Blocks() as app: |
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gr.HTML(""" |
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<div style="z-index: 100; position: fixed; top: 0px; right: 0px; left: 0px; bottom: 0px; width: 100%; height: 100%; background: white; display: flex; align-items: center; justify-content: center; color: black;"> |
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<div style="text-align: center; color: black;"> |
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<p style="color: black;">This space is a headless component of the cloud rendering engine used by AiTube.</p> |
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<p style="color: black;">It is not available for public use, but you can use the <a href="https://huggingface.co/spaces/ByteDance/AnimateDiff-Lightning" target="_blank">original space</a>.</p> |
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</div> |
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</div>""") |
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secret_token = gr.Textbox(label="Secret token") |
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input_text = gr.Textbox(label="Input Text") |
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description = gr.Textbox(label="Description") |
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run_button = gr.Button("Generate Audio") |
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audio_out = gr.Textbox() |
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inputs = [secret_token, input_text, description] |
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outputs = [audio_out] |
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run_button.click(fn=gen_tts, inputs=inputs, outputs=outputs, queue=True) |
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app.queue() |
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app.launch() |
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