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| <title>XTTVS-MED: Data-Driven Voice Cloning for Healthcare</title> | |
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| <!-- Header --> | |
| <header class="text-center mb-5"> | |
| <h1>XTTVS-MED</h1> | |
| <p class="lead">Real-time 4-Bit Semantic Voice Cloning & Voice-to-Voice Translation</p> | |
| <p><strong>Chris Coleman</strong> — GhostAI Labs<br> | |
| <strong>Dr. Anthony Becker, M.D.</strong> — Medical Advisor | |
| </p> | |
| </header> | |
| <!-- Overview --> | |
| <section id="overview" class="mb-5"> | |
| <h2>1. Overview</h2> | |
| <p> | |
| XTTVS-MED fuses Whisper ASR, 4-bit quantization, LoRA adapters, and a float-aligned CBR-RTree scheduler | |
| to deliver sub-second, emotion-aware, multilingual voice-to-voice translation on devices ≥6 GB VRAM. | |
| </p> | |
| <div class="diagram mermaid"> | |
| flowchart LR | |
| A["Input Audio"] --> W["Whisper ASR<br/>(Transcribe/Detect Lang)"] | |
| W --> S["Normalize & Preprocess<br/>(Mel-Spectrogram)"] | |
| S --> L["LoRA Adapters<br/>(Speaker/Emotion/Urgency)"] | |
| L --> Q["FloatBin Quantization<br/>(FP32→FP16→INT4)"] | |
| Q --> C["CBR-RTree Scheduler<br/>(Urgency/Pitch/Emotion)"] | |
| C --> M["XTTSv2 Transformer"] | |
| M --> V["Vocoder<br/>(WaveRNN/HiFiGAN)"] | |
| V --> B["Output Audio"] | |
| </div> | |
| </section> | |
| <!-- Architecture --> | |
| <section id="architecture" class="mb-5"> | |
| <h2>2. Architecture & Data Flow</h2> | |
| <div class="diagram mermaid"> | |
| sequenceDiagram | |
| participant U as User | |
| participant G as Gradio UI | |
| participant A as FastAPI | |
| participant W as Whisper | |
| participant M as TTS Server | |
| participant D as Disk(outputs/) | |
| U->>G: Record/Input Audio | |
| G->>A: POST /voice2voice | |
| A->>W: Whisper.transcribe(audio) | |
| W-->>A: text + lang | |
| A->>M: gen_voice(text, lang, settings) | |
| M-->>A: synthesized audio + metrics | |
| A->>G: return output audio & info | |
| A->>D: save MP3 | |
| </div> | |
| <pre> | |
| // Pseudocode: Voice-to-Voice pipeline with CBR-RTree | |
| def voice2voice(audio): | |
| text, lang = whisper.transcribe(audio) | |
| v4, t_fp = preprocess(text) | |
| node = insert(None, v4, t_fp) | |
| best = retrieve(node, t_fp) | |
| return tts.generate(text, adapter=best.adapter) | |
| </pre> | |
| </section> | |
| <!-- Performance --> | |
| <section id="performance" class="mb-5"> | |
| <h2>3. Hardware Scalability & Throughput</h2> | |
| <p>On-premise, HIPAA/GDPR compliant, supporting:</p> | |
| <div class="diagram mermaid"> | |
| flowchart TB | |
| HF200["HF200 Cluster<br/>0.15 s"] --> H100["DGX H100<br/>0.25 s"] | |
| H100 --> DGX["DGX Station<br/>0.4 s"] | |
| DGX --> RTX["RTX 2060<br/>1.5 s"] | |
| RTX --> TPU["Helios 8 TPU<br/>3.2 s"] | |
| </div> | |
| <div class="table-responsive"> | |
| <table class="table table-dark table-striped"> | |
| <thead> | |
| <tr> | |
| <th>Device</th><th>Compute</th><th>Memory</th><th>Min VRAM</th> | |
| <th>Latency</th><th>Streams</th><th>Bandwidth</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <td>Pi 5 + Helios 8 TPU</td><td>26 TFLOPS</td><td>4 GB LPDDR4</td><td>—</td> | |
| <td>3.2 s</td><td>1–2</td><td>200 GB/s</td> | |
| </tr> | |
| <tr> | |
| <td>RTX 2060</td><td>6 TFLOPS</td><td>6 GB GDDR6</td><td>6 GB</td> | |
| <td>1.5 s</td><td>1–2</td><td>200 GB/s</td> | |
| </tr> | |
| <tr> | |
| <td>DGX Station</td><td>1 000 TFLOPS</td><td>128 GB HBM2e</td><td>6 GB</td> | |
| <td>0.4 s</td><td>20–30</td><td>800 GB/s</td> | |
| </tr> | |
| <tr> | |
| <td>DGX H100</td><td>2 000 TFLOPS</td><td>640 GB HBM3</td><td>6 GB</td> | |
| <td>0.25 s</td><td>40–60</td><td>2 000 GB/s</td> | |
| </tr> | |
| <tr> | |
| <td>HF200 Cluster</td><td>5 000 TFLOPS</td><td>1.3 PB HBM3</td><td>6 GB</td> | |
| <td>0.15 s</td><td>100+</td><td>4 000 GB/s</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| </div> | |
| </section> | |
| <!-- Translation + Quick LoRA --> | |
| <section id="translation" class="mb-5"> | |
| <h2>4. Quick LoRA Epoch Training</h2> | |
| <p> | |
| For unsupported dialects: record 1–2 hrs of local speech, then train LoRA adapters—5–10 epochs in <strong>30 min</strong>—to extend coverage instantly. | |
| </p> | |
| <div class="diagram mermaid"> | |
| flowchart LR | |
| D["Dialect Samples (1–2 hrs)"] | |
| --> P["Preprocess & Align"] | |
| --> T["Train LoRA Epochs<br/>(5–10)"] | |
| --> U["Updated Adapters"] | |
| --> I["Immediate Inference"] | |
| </div> | |
| <ul> | |
| <li>Step 1: Capture ~1 hr dialect audio.</li> | |
| <li>Step 2: Generate aligned spectrograms.</li> | |
| <li>Step 3: Fine-tune LoRA adapters (30 min).</li> | |
| <li>Step 4: Deploy instantly for voice-to-voice.</li> | |
| </ul> | |
| </section> | |
| <!-- Clinical Impact --> | |
| <section id="impact" class="mb-5"> | |
| <h2>5. Clinical Impact & Validation</h2> | |
| <p> | |
| Every second saved reduces mortality by ~7%. | |
| Audio-to-audio translation in <1 s can improve survival by 10–15% for non-native speakers. | |
| </p> | |
| <div class="row"> | |
| <div class="col-md-6"> | |
| <div class="accessibility-box"> | |
| ⚠️ “Blood pressure critically low—initiate IV fluids immediately.”<br/> | |
| [Dual-text & audio UI] | |
| </div> | |
| </div> | |
| <div class="col-md-6"> | |
| <p><strong>Dataset & Metrics:</strong></p> | |
| <ul> | |
| <li>600 hrs clinical dialogues</li> | |
| <li>ANOVA on MOS (p < 0.01)</li> | |
| <li>Speaker similarity ≥ 92%; MOS intelligibility ≥ 4.5/5</li> | |
| </ul> | |
| </div> | |
| </div> | |
| </section> | |
| <!-- BibTeX --> | |
| <section id="bibtex" class="mb-5"> | |
| <h2>6. BibTeX</h2> | |
| <pre>@article{coleman2025xttvmed, | |
| author = {Coleman, Chris and Becker, Anthony}, | |
| title = {XTTVS-MED: Real-Time Voice-to-Voice Semantic Cloning to Prevent Medical Miscommunication}, | |
| journal = {GhostAI Labs}, | |
| year = {2025} | |
| }</pre> | |
| </section> | |
| </div> | |
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| <p>© 2025 GhostAI Labs — <a href="https://huggingface.co/spaces/ghostai1/GHOSTVOICECBR" target="_blank">Live Demo</a></p> | |
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