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Update app.py
Browse files
app.py
CHANGED
@@ -3,326 +3,213 @@ import torch
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import torchaudio
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import numpy as np
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from transformers import AutoProcessor, SeamlessM4Tv2Model
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from datetime import datetime
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import time
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class
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def __init__(self
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self.
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self.sample_rate = self.model.config.sampling_rate
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self.
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"English
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"Spanish
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"French
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"German
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"Italian
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"Portuguese
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"Russian
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"Chinese
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"Japanese
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"Korean
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"Hindi (IN)": "hin",
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"Arabic (AR)": "ara"
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}
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def
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try:
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if audio_path is None:
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raise gr.Error("No audio input provided")
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# Carregar e resample do áudio
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audio, orig_freq = torchaudio.load(audio_path)
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audio = torchaudio.functional.resample(audio, orig_freq=orig_freq, new_freq=16000)
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inputs = self.processor(audios=audio, return_tensors="pt")
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audio_array = self.model.generate(**inputs, tgt_lang=self.
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raise gr.Error(f"Audio processing failed: {str(e)}")
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def translate_text(self, text: str, src_lang: str, tgt_lang: str) -> tuple[int, np.ndarray]:
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try:
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if not text.strip():
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raise gr.Error("No text input provided")
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inputs = self.processor(text=text, src_lang=self.language_codes[src_lang], return_tensors="pt")
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audio_array = self.model.generate(**inputs, tgt_lang=self.language_codes[tgt_lang])[0].cpu().numpy().squeeze()
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return self.sample_rate, audio_array
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except Exception as e:
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raise gr.Error(
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css = """
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--accent: #ff3366;
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--background: #000000;
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--text: #ffffff;
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}
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#aris-interface {
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background-color: var(--background);
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background-image:
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radial-gradient(circle at 20% 20%, rgba(0, 102, 204, 0.1) 0%, transparent 50%),
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radial-gradient(circle at 80% 80%, rgba(0, 255, 255, 0.1) 0%, transparent 50%);
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min-height: 100vh;
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font-family: 'Courier New', monospace;
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padding: 20px;
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}
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.
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margin-bottom: 30px;
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position: relative;
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}
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.title-container h1 {
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font-size: 3em;
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letter-spacing: 10px;
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margin: 0;
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text-shadow: 0 0 10px var(--primary);
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}
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.title-container h3 {
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font-size: 1.2em;
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letter-spacing: 3px;
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opacity: 0.8;
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margin: 5px 0;
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}
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height: 400px;
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border: 4px solid var(--primary);
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border-radius: 50%;
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margin: 20px auto;
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position: relative;
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animation: pulse 2s infinite;
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display: flex;
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align-items: center;
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justify-content: center;
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background:
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radial-gradient(circle at center, rgba(0, 255, 255, 0.1) 0%, transparent 70%),
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conic-gradient(from 0deg, transparent 0%, rgba(0, 255, 255, 0.1) 50%, transparent 100%);
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}
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border: 1px solid rgba(0, 255, 255, 0.3);
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animation: rotate 20s linear infinite;
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}
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}
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70% { box-shadow: 0 0 0 20px rgba(0, 255, 255, 0); }
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100% { box-shadow: 0 0 0 0 rgba(0, 255, 255, 0); }
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}
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.
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border: 2px solid var(--primary) !important;
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color: var(--primary) !important;
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font-family: 'Courier New', monospace !important;
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border-radius: 5px !important;
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padding: 10px !important;
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}
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.
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border-radius: 5px !important;
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transition: all 0.3s ease !important;
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}
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.
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}
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.
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color: var(--primary) !important;
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padding: 15px !important;
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border-radius: 5px !important;
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margin: 5px !important;
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text-align: center !important;
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text-transform: uppercase !important;
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letter-spacing: 1px !important;
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transition: all 0.3s ease !important;
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position: relative;
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overflow: hidden;
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}
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.
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left: -100%;
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width: 100%;
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height: 2px;
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background: linear-gradient(90deg, transparent, var(--primary));
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animation: scan-line 2s linear infinite;
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}
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}
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"""
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def
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translator =
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)
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<div class="mode-indicator">QUANTUM CORE ACTIVE</div>
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</div>
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''')
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with gr.Column(elem_id="aris-interface"):
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gr.HTML("""
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<div id="status-ring">
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<div id="outer-ring-decoration"></div>
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<div id="inner-ring">
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<div id="core">
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<div>A.R.I.S.</div>
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<div>QUANTUM CORE</div>
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<div>v2.0.0</div>
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<div class="system-version">NEURAL ENGINE ACTIVE</div>
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</div>
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</div>
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</div>
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""")
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with gr.Row():
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with gr.Column():
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with gr.Tab("Text Translation"):
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text_input = gr.Textbox(
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label="
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placeholder="Enter text
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lines=3
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)
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with gr.Row():
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choices=list(translator.
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value="English
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label="
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elem_classes=["aris-textbox"]
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)
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choices=list(translator.
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value="Spanish
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label="
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elem_classes=["aris-textbox"]
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)
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translate_btn = gr.Button("
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with gr.
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audio_input = gr.Audio(
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label="
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type="filepath"
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)
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tgt_lang_audio = gr.Dropdown(
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choices=list(translator.
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value="English
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label="
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elem_classes=["aris-textbox"]
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)
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translate_audio_btn = gr.Button("
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with gr.Column():
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audio_output = gr.Audio(
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label="TRANSLATION OUTPUT",
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type="numpy"
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)
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with gr.Row():
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with gr.Column(min_width=200):
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gr.HTML(
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"""
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<div class="status-box">
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NEURAL CORE<br>
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<strong>OPERATIONAL</strong>
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</div>
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"""
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)
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with gr.Column(min_width=200):
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gr.HTML(
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"""
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<div class="status-box">
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QUANTUM ENGINE<br>
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<strong>ACTIVE</strong>
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</div>
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"""
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)
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with gr.Row():
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with gr.Column(min_width=200):
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gr.HTML(
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"""
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<div class="status-box">
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TRANSLATION MATRIX<br>
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<strong>CALIBRATED</strong>
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</div>
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"""
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)
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with gr.Column(min_width=200):
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gr.HTML(
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"""
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<div class="status-box">
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VOICE SYNTHESIS<br>
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<strong>READY</strong>
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</div>
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"""
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)
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translate_audio_btn.click(
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fn=translator.process_audio,
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inputs=[audio_input, tgt_lang_audio],
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outputs=audio_output
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)
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return demo
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if __name__ == "__main__":
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demo =
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demo.queue()
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demo.launch()
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import torchaudio
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import numpy as np
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from transformers import AutoProcessor, SeamlessM4Tv2Model
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class SeamlessTranslator:
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def __init__(self):
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self.model_name = "facebook/seamless-m4t-v2-large"
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print("Loading model...")
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self.processor = AutoProcessor.from_pretrained(self.model_name)
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self.model = SeamlessM4Tv2Model.from_pretrained(self.model_name)
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self.sample_rate = self.model.config.sampling_rate
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self.languages = {
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"English": "eng",
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"Spanish": "spa",
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"French": "fra",
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"German": "deu",
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"Italian": "ita",
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"Portuguese": "por",
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"Russian": "rus",
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"Chinese": "cmn",
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"Japanese": "jpn",
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"Korean": "kor"
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}
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def translate_text(self, text, src_lang, tgt_lang, progress=gr.Progress()):
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progress(0.3, desc="Processing input...")
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try:
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inputs = self.processor(text=text, src_lang=self.languages[src_lang], return_tensors="pt")
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progress(0.6, desc="Generating audio...")
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audio_array = self.model.generate(**inputs, tgt_lang=self.languages[tgt_lang])[0].cpu().numpy().squeeze()
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progress(1.0, desc="Done!")
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return (self.sample_rate, audio_array)
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except Exception as e:
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raise gr.Error(str(e))
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def translate_audio(self, audio_path, tgt_lang, progress=gr.Progress()):
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progress(0.3, desc="Loading audio...")
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try:
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audio, orig_freq = torchaudio.load(audio_path)
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audio = torchaudio.functional.resample(audio, orig_freq=orig_freq, new_freq=16000)
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progress(0.6, desc="Translating...")
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inputs = self.processor(audios=audio, return_tensors="pt")
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audio_array = self.model.generate(**inputs, tgt_lang=self.languages[tgt_lang])[0].cpu().numpy().squeeze()
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progress(1.0, desc="Done!")
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return (self.sample_rate, audio_array)
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except Exception as e:
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raise gr.Error(str(e))
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css = """
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#component-0 {
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max-width: 1200px;
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margin: auto;
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padding: 20px;
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}
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.container {
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border-radius: 12px;
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padding: 20px;
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}
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.gr-form {
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border-color: #e5e7eb !important;
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}
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.gr-button {
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border-radius: 8px !important;
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background: linear-gradient(to right, #2563eb, #4f46e5) !important;
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color: white !important;
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font-weight: 600 !important;
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}
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.gr-button:hover {
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box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1) !important;
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transform: translateY(-1px);
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}
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.gr-input, .gr-select {
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border-radius: 8px !important;
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}
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.gr-panel {
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border-radius: 12px !important;
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}
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.title {
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text-align: center;
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font-size: 2.5rem;
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font-weight: bold;
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margin: 1rem 0;
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background: linear-gradient(to right, #2563eb, #4f46e5);
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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}
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.subtitle {
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text-align: center;
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color: #6b7280;
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margin-bottom: 2rem;
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}
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.tab-nav {
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border-bottom: 2px solid #e5e7eb;
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margin-bottom: 1rem;
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}
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.output-label {
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font-weight: 600;
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color: #374151;
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margin-bottom: 0.5rem;
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}
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.footer {
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text-align: center;
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margin-top: 2rem;
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padding-top: 1rem;
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border-top: 1px solid #e5e7eb;
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color: #6b7280;
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font-size: 0.875rem;
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}
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"""
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+
def create_ui():
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127 |
+
translator = SeamlessTranslator()
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+
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+
with gr.Blocks(css=css, title="A.R.I.S. Translator") as demo:
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gr.HTML(
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+
"""
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+
<div class="title">A.R.I.S. Translator</div>
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+
<div class="subtitle">Advanced Real-time Interpretation System</div>
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+
"""
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)
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+
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+
with gr.Tabs() as tabs:
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+
# Text to Speech Tab
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+
with gr.Tab("Text Translation", id=1):
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+
with gr.Row():
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+
with gr.Column():
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text_input = gr.Textbox(
|
143 |
+
label="Text to Translate",
|
144 |
+
placeholder="Enter your text here...",
|
145 |
+
lines=5
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|
146 |
)
|
147 |
with gr.Row():
|
148 |
+
src_lang = gr.Dropdown(
|
149 |
+
choices=list(translator.languages.keys()),
|
150 |
+
value="English",
|
151 |
+
label="Source Language"
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|
152 |
)
|
153 |
+
tgt_lang = gr.Dropdown(
|
154 |
+
choices=list(translator.languages.keys()),
|
155 |
+
value="Spanish",
|
156 |
+
label="Target Language"
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|
157 |
)
|
158 |
+
translate_btn = gr.Button("Translate", variant="primary")
|
159 |
+
|
160 |
+
with gr.Column():
|
161 |
+
gr.HTML('<div class="output-label">Translation Output</div>')
|
162 |
+
audio_output = gr.Audio(
|
163 |
+
label="Translated Audio",
|
164 |
+
type="numpy"
|
165 |
+
)
|
166 |
+
|
167 |
+
# Audio to Speech Tab
|
168 |
+
with gr.Tab("Audio Translation", id=2):
|
169 |
+
with gr.Row():
|
170 |
+
with gr.Column():
|
171 |
audio_input = gr.Audio(
|
172 |
+
label="Upload Audio",
|
173 |
type="filepath"
|
174 |
)
|
175 |
tgt_lang_audio = gr.Dropdown(
|
176 |
+
choices=list(translator.languages.keys()),
|
177 |
+
value="English",
|
178 |
+
label="Target Language"
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|
179 |
)
|
180 |
+
translate_audio_btn = gr.Button("Translate Audio", variant="primary")
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|
181 |
|
182 |
+
with gr.Column():
|
183 |
+
gr.HTML('<div class="output-label">Translation Output</div>')
|
184 |
+
audio_output_from_audio = gr.Audio(
|
185 |
+
label="Translated Audio",
|
186 |
+
type="numpy"
|
187 |
+
)
|
188 |
+
|
189 |
+
gr.HTML(
|
190 |
+
"""
|
191 |
+
<div class="footer">
|
192 |
+
Powered by Meta's SeamlessM4T model | Built with Gradio
|
193 |
+
</div>
|
194 |
+
"""
|
195 |
+
)
|
196 |
+
|
197 |
+
# Event handlers
|
198 |
+
translate_btn.click(
|
199 |
+
fn=translator.translate_text,
|
200 |
+
inputs=[text_input, src_lang, tgt_lang],
|
201 |
+
outputs=audio_output
|
202 |
+
)
|
203 |
+
|
204 |
+
translate_audio_btn.click(
|
205 |
+
fn=translator.translate_audio,
|
206 |
+
inputs=[audio_input, tgt_lang_audio],
|
207 |
+
outputs=audio_output_from_audio
|
208 |
+
)
|
209 |
|
|
|
|
|
|
|
|
|
|
|
|
|
210 |
return demo
|
211 |
|
212 |
if __name__ == "__main__":
|
213 |
+
demo = create_ui()
|
214 |
demo.queue()
|
215 |
demo.launch()
|