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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> &mdash; GhostAI Labs<br>
<strong>Dr. Anthony Becker, M.D.</strong> &mdash; 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 &lt; 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>
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