Spaces:
Running
Running
File size: 18,517 Bytes
90fcad7 a8a8f61 90fcad7 a8a8f61 90fcad7 a8a8f61 90fcad7 a8a8f61 90fcad7 a8a8f61 90fcad7 a8a8f61 a6e3f27 a8a8f61 dc24f1a a8a8f61 452aeee a8a8f61 cfbb9d5 a8a8f61 90fcad7 a8a8f61 90fcad7 a8a8f61 a6e3f27 a8a8f61 cfbb9d5 a8a8f61 90fcad7 a8a8f61 90fcad7 a8a8f61 90fcad7 a8a8f61 90fcad7 a8a8f61 90fcad7 a8a8f61 21a3273 a8a8f61 21a3273 a8a8f61 ad52334 21a3273 ad52334 21a3273 ad52334 21a3273 ad52334 21a3273 ad52334 21a3273 ad52334 a8a8f61 8445001 a8a8f61 8445001 7743176 8445001 7743176 8445001 7743176 8445001 7743176 8445001 7743176 a8a8f61 21a3273 a8a8f61 21a3273 a8a8f61 21a3273 a8a8f61 21a3273 a8a8f61 21a3273 a8a8f61 21a3273 a8a8f61 21a3273 a8a8f61 cfbb9d5 a8a8f61 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 |
# ---------------------------------------------------------------
# app.py – "TTS Showcase" (Gradio Implementation)
# ---------------------------------------------------------------
import os
import gradio as gr
# ---------- 1. Demo metadata ----------
MODELS = {
"nari-labs/Dia-1.6B": "Dia-1.6B",
"hexgrad/Kokoro-82M": "Kokoro-82M",
"sesame/csm-1b": "csm-1b",
"SparkAudio/Spark-TTS-0.5B": "Spark-TTS-0.5B",
"canopylabs/orpheus-3b-0.1-ft": "Orpheus-3b-0.1-ft",
"SWivid/F5-TTS": "F5-TTS",
"Zyphra/Zonos-v0.1-transformer": "Zonos-v0.1-transformer",
"coqui/XTTS-v2": "XTTS-v2",
"HKUSTAudio/Llasa-3B": "Llasa-3B",
"amphion/MaskGCT": "MaskGCT",
"OuteAI/Llama-OuteTTS-1.0-1B": "Llama-OuteTTS-1.0-1B",
"ByteDance/MegaTTS3": "MegaTTS3"
}
# Performance ratings for each model
MODEL_RATINGS = {
"nari-labs/Dia-1.6B": {"naturalness": "Good", "intelligibility": "Moderate", "controllability": "Good"},
"hexgrad/Kokoro-82M": {"naturalness": "Good", "intelligibility": "Excellent", "controllability": "Moderate"},
"sesame/csm-1b": {"naturalness": "Excellent", "intelligibility": "Excellent", "controllability": "Good"},
"SparkAudio/Spark-TTS-0.5B": {"naturalness": "Excellent", "intelligibility": "Excellent", "controllability": "Moderate"},
"canopylabs/orpheus-3b-0.1-ft": {"naturalness": "Excellent", "intelligibility": "Excellent", "controllability": "Moderate"},
"SWivid/F5-TTS": {"naturalness": "Excellent", "intelligibility": "Excellent", "controllability": "Good"},
"Zyphra/Zonos-v0.1-transformer": {"naturalness": "Good", "intelligibility": "Moderate", "controllability": "Excellent"},
"coqui/XTTS-v2": {"naturalness": "Good", "intelligibility": "Excellent", "controllability": "Moderate"},
"HKUSTAudio/Llasa-3B": {"naturalness": "Excellent", "intelligibility": "Good", "controllability": "Moderate"},
"amphion/MaskGCT": {"naturalness": "Good", "intelligibility": "Excellent", "controllability": "Moderate"},
"OuteAI/Llama-OuteTTS-1.0-1B": {"naturalness": "Moderate", "intelligibility": "Moderate", "controllability": "Moderate"},
"ByteDance/MegaTTS3": {"naturalness": "Good", "intelligibility": "Good", "controllability": "Moderate"}
}
# Model descriptions for better understanding
MODEL_DESCRIPTIONS = {
"nari-labs/Dia-1.6B": "Expressive conversational voice with moderate quality",
"hexgrad/Kokoro-82M": "Lightweight powerhouse with excellent clarity",
"sesame/csm-1b": "High-quality synthesis with excellent naturalness",
"SparkAudio/Spark-TTS-0.5B": "Efficient model with excellent performance",
"canopylabs/orpheus-3b-0.1-ft": "Fine-tuned large model with superior quality",
"SWivid/F5-TTS": "Advanced flow-based synthesis with top ratings",
"Zyphra/Zonos-v0.1-transformer": "Highly controllable transformer-based model",
"coqui/XTTS-v2": "Multi-lingual excellence with proven performance",
"HKUSTAudio/Llasa-3B": "Large-scale audio synthesis model",
"amphion/MaskGCT": "Masked generative modeling approach",
"OuteAI/Llama-OuteTTS-1.0-1B": "LLM-based TTS with moderate performance",
"ByteDance/MegaTTS3": "Industrial-grade TTS solution"
}
# Folder that contains subfolders with the audio clips
SAMPLES_DIR = "samples"
CLIP_NAME = "generated-audio.wav"
# Test prompt used for evaluation
TEST_PROMPT = "Hello, this is a universal test sentence. Can the advanced Zylophonic system clearly articulate this and express a hint of excitement? The quick brown fox certainly hopes so!"
def repo_to_slug(repo: str) -> str:
"""Convert huggingface/xxx to huggingface_xxx for folder naming."""
return repo.replace("/", "_")
def get_rating_emoji(rating: str) -> str:
"""Convert rating to emoji."""
if rating == "Excellent":
return "🟢"
elif rating == "Good":
return "🟡"
else:
return "🟠"
def get_audio_path(repo: str) -> str:
"""Get the audio file path for a given repository."""
audio_path = os.path.join(SAMPLES_DIR, repo_to_slug(repo), CLIP_NAME)
return audio_path if os.path.isfile(audio_path) else None
def filter_models(search_term: str):
"""Filter models based on search term."""
if not search_term.strip():
return list(MODELS.keys())
search_lower = search_term.lower().strip()
return [
repo for repo, name in MODELS.items()
if search_lower in repo.lower() or search_lower in name.lower()
]
def create_model_card(repo: str) -> str:
"""Create a formatted model card with ratings and description."""
display_name = MODELS[repo]
description = MODEL_DESCRIPTIONS.get(repo, "High-quality TTS model")
ratings = MODEL_RATINGS.get(repo, {})
card_html = f"""
<div class="model-card" style="border: 1px solid #ddd; border-radius: 12px; padding: 20px; margin: 10px 0; background: white;">
<h3 style="color: #2c3e50; margin-top: 0;">🎤 {display_name}</h3>
</div>
"""
return card_html
# ---------- 2. Custom CSS ----------
custom_css = """
#title {
text-align: center;
background: rgb(203, 255, 77);
color: white;
padding: 2rem;
border-radius: 15px;
margin-bottom: 2rem;
}
#intro-section {
background: #f8f9fa;
color: #2c3e50;
padding: 1.5rem;
border-radius: 10px;
margin: 1rem 0;
border-left: 4px solid rgb(0, 72, 10);
}
#intro-section h2,
#intro-section h3 {
color: #2c3e50;
}
#intro-section p {
color: #34495e;
}
#intro-section ul li {
color: #34495e;
}
#intro-section .mission-text {
color: #667eea !important;
font-weight: bold;
text-align: center;
}
#intro-section strong {
color: #2c3e50 !important;
}
#intro-section em {
color: #2c3e50 !important;
}
#intro-section .mission-text strong {
color: #667eea !important;
}
#test-prompt {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
padding: 1.5rem;
border-radius: 10px;
text-align: center;
margin: 1rem 0;
}
.model-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(400px, 1fr));
gap: 1rem;
margin: 1rem 0;
}
#footer {
text-align: center;
padding: 2rem;
color: #666;
border-top: 1px solid #eee;
margin-top: 2rem;
}
/* make all the text in our white‐background cards dark */
.model-grid .gr-html * {
color: #2c3e50 !important;
}
.model-card {
background: white;
color: #2c3e50 !important;
border: 1px solid #ddd;
border-radius: 12px;
padding: 20px;
margin: 10px 0;
}
"""
# ---------- 3. Main Gradio Interface ----------
def create_interface():
with gr.Blocks(css=custom_css, title="🎙️ TTS Model Gallery", theme=gr.themes.Soft()) as demo:
# Header Section
gr.HTML("""
<div id="title">
<h1>🎙️ Open-Source Text-to-Speech Model Gallery</h1>
</div>
""")
# Introduction Section
gr.HTML("""
<div id="intro-section">
<h3>🔬 Our Exciting Quest</h3>
<p>We’re on a mission to help developers quickly find and compare the best open-source TTS models for their audio projects. In this gallery, you’ll find 12 state-of-the-art TTS models, each evaluated using a consistent test prompt to assess their synthesized speech.</p>
<p><strong>Featured TTS Models:</strong></p>
<ul>
<li>🎭 <strong>Dia-1.6B</strong> - Expressive conversational voice</li>
<li>🎪 <strong>Kokoro-82M</strong> - Lightweight powerhouse</li>
<li>🎨 <strong>F5-TTS</strong> - Advanced flow-based synthesis</li>
<li>🎵 <strong>XTTS-v2</strong> - Multi-lingual excellence</li>
<li>🎼 <strong>MaskGCT</strong> - Masked generative modeling</li>
<li>🎤 <strong>Llasa-3B</strong> - Large-scale audio synthesis</li>
<li><em>...and 6 more incredible models!</em></li>
</ul>
<h3>🔑 Key Findings</h3>
<ol>
<li><strong>Outstanding Speech Quality</strong><br>
Several models—namely <strong>Kokoro-82M</strong>, <strong>csm-1b</strong>, <strong>Spark-TTS-0.5B</strong>,
<strong>Orpheus-3b-0.1-ft</strong>, <strong>F5-TTS</strong>, and <strong>Llasa-3B</strong> delivered exceptionally
natural, clear, and realistic synthesized speech. Among these, <strong>csm-1b</strong> and <strong>F5-TTS</strong>
stood out as the most well-rounded model as they combined good synthesized speech with solid controllability.
</li>
<li><strong>Superior Controllability</strong><br>
<strong>Zonos-v0.1-transformer</strong> emerged as the best in fine-grained control: it offers detailed
adjustments for prosody, emotion, and audio quality, making it ideal for use cases that demand precise
voice modulation.
</li>
<li><strong>Performance vs. Footprint Trade-off</strong><br>
Smaller models (e.g., <strong>Kokoro-82M</strong> at 82 million parameters) can still excel in many scenarios, especially when efficient inference or low VRAM usage is critical.
Larger models (1 billion–3 billion+ parameters) generally offer more versatility—handling multilingual
synthesis, zero-shot voice cloning, and multi-speaker generation but require heavier compute resources.
</li>
<li><strong>Special Notes on Multilingual & Cloning Capabilities</strong><br>
<strong>Spark-TTS-0.5B</strong> and <strong>XTTS-v2</strong> excel at cross-lingual and zero-shot voice
cloning, making them strong candidates for projects that need multi-language support or short-clip cloning.
<strong>Llama-OuteTTS-1.0-1B</strong> and <strong>MegaTTS3</strong> also offer multilingual input handling,
though they may require careful sampling parameter tuning to achieve optimal results.
</li>
</ol>
</div>
""")
# Test Prompt Section
# gr.HTML(f"""
# <div id="test-prompt">
# <h3>🎯 Universal Test Prompt</h3>
# <p style="font-style: italic; font-size: 1.1em;">"{TEST_PROMPT}"</p>
# <p style="font-size: 0.9em; opacity: 0.9;">
# Carefully crafted to test naturalness, intelligibility, and technical pronunciation across all models
# </p>
# </div>
# """)
# Evaluation Criteria
# with gr.Row():
# with gr.Column():
# gr.HTML("""
# <div style="text-align: center; padding: 1rem; background: rgba(102, 126, 234, 0.1); border-radius: 8px;">
# <div style="font-size: 2rem;">🎭</div>
# <strong>Naturalness</strong><br>
# <small>Human-like quality & emotional expression</small>
# </div>
# """)
# with gr.Column():
# gr.HTML("""
# <div style="text-align: center; padding: 1rem; background: rgba(102, 126, 234, 0.1); border-radius: 8px;">
# <div style="font-size: 2rem;">🗣️</div>
# <strong>Intelligibility</strong><br>
# <small>Clarity & pronunciation accuracy</small>
# </div>
# """)
# with gr.Column():
# gr.HTML("""
# <div style="text-align: center; padding: 1rem; background: rgba(102, 126, 234, 0.1); border-radius: 8px;">
# <div style="font-size: 2rem;">🎛️</div>
# <strong>Controllability</strong><br>
# <small>Tone, pace & parameter flexibility</small>
# </div>
# """)
# gr.Markdown("---")
# gr.Markdown("""
# ## 🔑 Key Findings
# 1. **Outstanding Speech Quality**
# Several models—namely **Kokoro-82M**, **csm-1b**, **Spark-TTS-0.5B**, **Orpheus-3b-0.1-ft**, **F5-TTS**, and **Llasa-3B**—delivered exceptionally natural, clear, and realistic synthesized speech. Among these, **csm-1b** and **F5-TTS** stood out as the most well-rounded: they combined top-tier naturalness and intelligibility with solid controllability.
# 2. **Superior Controllability**
# **Zonos-v0.1-transformer** emerged as the leader in fine-grained control: it offers detailed adjustments for prosody, emotion, and audio quality, making it ideal for use cases that demand precise voice modulation.
# 3. **Performance vs. Footprint Trade-off**
# Smaller models (e.g., **Kokoro-82M** at 82 million parameters) can still achieve “Good” or “Excellent” ratings in many scenarios, especially when efficient inference or low VRAM usage is critical. Larger models (1 billion–3 billion+ parameters) generally offer more versatility—handling multilingual synthesis, zero-shot voice cloning, and multi-speaker generation—but require heavier compute resources.
# 4. **Special Notes on Multilingual & Cloning Capabilities**
# **Spark-TTS-0.5B** and **XTTS-v2** excel at cross-lingual and zero-shot voice cloning, making them strong candidates for projects that need multi-language support or short-clip cloning. **Llama-OuteTTS-1.0-1B** and **MegaTTS3** also offer multilingual input handling, though they may require careful sampling parameter tuning to achieve optimal results.
# """)
# Search and Filter Section
with gr.Row():
search_box = gr.Textbox(
label="🔍 Search Models",
placeholder="Filter by name or family (e.g., 'F5', 'TTS', '3B')",
value="",
scale=3
)
clear_btn = gr.Button("Clear", scale=1)
# Model Gallery Section
gr.Markdown("## 🎧 Model Gallery")
# Create model cards and audio players
model_components = []
for repo, display_name in MODELS.items():
with gr.Group():
# Model information card
model_info = gr.HTML(create_model_card(repo))
# Audio player
audio_path = get_audio_path(repo)
if audio_path:
audio_player = gr.Audio(
value=audio_path,
label=f"🎵 {display_name} Audio Sample",
interactive=False
)
else:
audio_player = gr.HTML(f"<p style='color: red;'>🤷♂️ Audio sample not found for {display_name}</p>")
model_components.append((repo, model_info, audio_player))
# Search functionality
def update_visibility(search_term):
filtered_repos = filter_models(search_term)
updates = []
for repo, model_info, audio_player in model_components:
visible = repo in filtered_repos
updates.extend([
gr.update(visible=visible), # model_info
gr.update(visible=visible) # audio_player
])
return updates
# Connect search functionality
search_box.change(
fn=update_visibility,
inputs=[search_box],
outputs=[comp for repo, model_info, audio_player in model_components for comp in [model_info, audio_player]]
)
clear_btn.click(
fn=lambda: "",
outputs=[search_box]
)
# Methodology Section
# with gr.Accordion("📋 Detailed Evaluation Methodology", open=False):
# gr.Markdown("""
# ### Test Prompt
# `Hello, this is a universal test sentence. Can the advanced Zylophonic system clearly articulate this and express a hint of excitement? The quick brown fox certainly hopes so!`
# ### Model Evaluation Criteria:
# 🎭 **Naturalness (Human-like Quality)**
# - Prosody and rhythm patterns
# - Emotional expression capability
# - Voice texture and warmth
# - Natural breathing and pauses
# 🗣️ **Intelligibility (Clarity & Accuracy)**
# - Word pronunciation precision
# - Consonant and vowel clarity
# - Sentence comprehensibility
# - Technical term handling
# 🎛️ **Controllability (Flexibility)**
# - Parameter responsiveness
# - Tone modification capability
# - Speed and pitch control
# - Customization potential
# ### Key Insights:
# - Smaller models (82M-500M) can excel in specific scenarios
# - Larger models (1B-3B+) offer more versatility but require more resources
# - Architecture matters as much as parameter count
# - Training data quality significantly impacts output quality
# """)
# Footer
# gr.HTML("""
# <div id="footer">
# <p><strong>🚀 Ready to deploy your own TTS model?</strong></p>
# <p>This demo showcases the power of open-source TTS technology. Each model offers unique strengths for different applications.</p>
# <p><em>Built with ❤️ using Gradio • All models are open-source and available on Hugging Face</em></p>
# <p>⚡ Powered by Inferless</p>
# </div>
# """)
return demo
# ---------- 4. Launch the application ----------
if __name__ == "__main__":
demo = create_interface()
demo.launch(
share=True,
inbrowser=True,
show_error=True
) |