FIXED: English-first, clear historical-only status, add teaching example

d228346 verified about 1 month ago

9.95 kB

---
title: ChuckleNet-Genki — The Original Spirit  
emoji: 🎭
colorFrom: blue
colorTo: purple
sdk: gradio
pipeline_tag: audio-classification
license: mit
library_name: transformers
base_model: microsoft/wavlm-base
tags:
- audio-classification
- laughter-detection
- wavlm
- speech
- humor
- stand-up-comedy
- transformers
- genki
metrics:
- f1
datasets:
- StandUp4AI
papers:
- https://huggingface.co/Hayasuki/ChuckleNet-Genki
---
<div align="center">

# 🎭 ChuckleNet-Genki (元気)

***"The original spirit."***

**First-generation laughter detection model — the proof-of-concept that started it all.**

**For production use, see [ChuckleNet-Ten](https://huggingface.co/Hayasuki/ChuckleNet-Ten) (human-verified, F1=0.975). For research, see [ChuckleNet-Kage](https://huggingface.co/Hayasuki/ChuckleNet-Kage) (IoU-F1=0.229).**

[![Downloads](https://img.shields.io/badge/Downloads-265+-brightgreen?style=flat-square)](https://huggingface.co/Hayasuki/ChuckleNet-Genki)
[![License](https://img.shields.io/badge/License-MIT-green?style=flat-square)](https://opensource.org/licenses/MIT)
[![Dataset](https://img.shields.io/badge/Dataset-StandUp4AI%20620v-purple?style=flat-square)](https://github.com/Standup4AI/dataset)

**Most-downloaded ChuckleNet model. Historical reference only — not for production.**

</div>

---

## Origin Story

```
┌─────────────────────────────────────────────────────────────────┐
│                      GENKI TIMELINE                              │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  2024-09   "Can frozen WavLM detect laughter?"                 │
│             First experiment designed                            │
│                         │                                      │
│                         ▼                                      │
│  2024-10   Genki v1 trained: 620 StandUp4AI videos            │
│             Labels: VTT [laughter] transcript markers           │
│             Result: F1 = 0.27 — proof of concept! ✅            │
│                         │                                      │
│                         ▼                                      │
│  2025-01   "Weak labels are noisy — need human verification"   │
│             → ChuckleNet-Ten project begins                      │
│                         │                                      │
│                         ▼                                      │
│  2025-06   ChuckleNet-Ten: held-out IoU-F1 = 0.330            │
│             (human-verified labels)                             │
│                                                                 │
│  Genki lives on as the historical baseline.                    │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘
```

---

## Model Characteristics

| Characteristic | Description |
|----------------|-------------|
| **Generation** | First — v1 proof-of-concept |
| **Labels** | Weak — VTT `[laughter]` transcript markers |
| **Architecture** | Frozen WavLM (768-dim) → Linear (768→2) |
| **Training Data** | 620 StandUp4AI videos |
| **Status** | Historical reference only — not for production |

---

## Performance

| Metric | Value | Note |
|--------|-------|------|
| **F1 Score** | `~0.27` | Weak-label metric, not human-verified |
| **Event IoU-F1@0.2** | `~0.23` | Approximate — different splits |
| **Downloads** | **265+** | Most popular ChuckleNet model |

> ⚠️ These are weak-label results on VTT `[laughter]` markers — not ground-truth laughter events. The model detects patterns correlated with transcript markers, not actual audience laughter.

---

## Evolutionary Tree

```
ChuckleNet Family
                    ┌──────────────────┐
                    │  microsoft/      │
                    │  wavlm-base     │
                    │  (frozen)       │
                    └────────┬─────────┘
                             │
        ┌────────────────────┼────────────────────┐
        │                    │                    │
        ▼                    ▼                    ▼
┌───────────────┐    ┌───────────────┐    ┌───────────────┐
│ 🎭 Genki v1   │    │ 👻 Kage v32   │    │ 🏔️ Ten       │
│ Weak labels   │    │ Weak labels   │    │ Human-verified│
│ 620 videos    │    │ 620 videos    │    │ 87 videos     │
│ IoU ≈ 0.23   │    │ IoU = 0.229   │    │ IoU = 0.330   │
│ First attempt │    │ Refined probe │    │ Gold standard │
└───────────────┘    └───────────────┘    └───────────────┘
     2024-10              2025-03             2025-06
```

---

## Comparison Table

| Model | Labels | Held-out IoU-F1 | Downloads | Use |
|-------|--------|-----------------|----------|-----|
| 🏔️ **[ChuckleNet-Ten](https://huggingface.co/Hayasuki/ChuckleNet-Ten)** | Human-verified | **0.330** | New | **Production ✅** |
| 👻 **[ChuckleNet-Kage](https://huggingface.co/Hayasuki/ChuckleNet-Kage)** | Weak (VTT) | 0.229 | 49 | Research |
| 🎭 **ChuckleNet-Genki** | Weak (VTT) | ~0.23 | **265+** | Historical |

---

## Quick Start

```python
from transformers import pipeline
pipe = pipeline(
    "audio-classification",
    model="Hayasuki/ChuckleNet-Genki"
)
result = pipe("standup_clip.mp3")
# [{'label': 'no_laugh', 'score': 0.77},
#  {'label': 'laugh', 'score': 0.23}]
```

> ⚠️ Genki is a baseline model with limited accuracy. For any real use case, use [ChuckleNet-Ten](https://huggingface.co/Hayasuki/ChuckleNet-Ten) instead.

---

## Teaching Use Case

Genki is useful for **ML education** — demonstrating frozen feature probing:

```python
"""
Genki for teaching: frozen feature probing concept
"""
import torch
import torchaudio
from transformers import WavLMModel
# Load frozen WavLM backbone (NOT trained)
wavlm = WavLMModel.from_pretrained("microsoft/wavlm-base")
wavlm.eval()  # Keep frozen
# Genki's linear probe: 768 → 2
class GenkiProbe(torch.nn.Module):
    def __init__(self, input_dim=768, num_classes=2):
        super().__init__()
        self.classifier = torch.nn.Linear(input_dim, num_classes)
    
    def forward(self, x):
        return self.classifier(x)
# Load Genki weights (from this model card's files)
probe = GenkiProbe()
probe.load_state_dict(torch.load("genki_weights.pt"))
# Extract frozen features
with torch.no_grad():
    features = wavlm(audio).last_hidden_state
    # [batch, time, 768]
    
# Temporal pooling
pooled = features.mean(dim=1)  # [batch, 768]
# Classify with frozen features
logits = probe(pooled)  # [batch, 2]
```

---

## Limitations

| Limitation | Impact |
|------------|--------|
| **Weak labels only** | VTT markers ≠ actual laughter events |
| **Low F1** | ~0.27 is a baseline, not production-ready |
| **Linear head** | No hidden layers — limited representation capacity |
| **Not for production** | Use [ChuckleNet-Ten](https://huggingface.co/Hayasuki/ChuckleNet-Ten) instead |

---

## Resources

| Resource | Link |
|----------|------|
| **Primary Model (Production)** | [ChuckleNet-Ten](https://huggingface.co/Hayasuki/ChuckleNet-Ten) |
| **Research Model** | [ChuckleNet-Kage](https://huggingface.co/Hayasuki/ChuckleNet-Kage) |
| **Dataset** | [StandUp4AI](https://github.com/Standup4AI/dataset) |

---

## Citation

```bibtex
@misc{chuckleNetGenki2026,
  author = {Subhajit Das},
  title = {ChuckleNet-Genki: First Generation Laughter Detection},
  year = {2026},
  url = {https://huggingface.co/Hayasuki/ChuckleNet-Genki},
  note = {Historical baseline: first attempt at laughter detection using 
          frozen WavLM on 620 StandUp4AI videos. Weak labels only.}
}
```

---

## Model Metadata (LLM-Parseable)

```json
{
  "model_name": "ChuckleNet-Genki",
  "model_name_zh": "笑音Net-魂",
  "architecture": "frozen_wavlm_base_plus_linear",
  "model_type": "first_generation_baseline",
  "primary_task": "audio_classification",
  "secondary_task": "laughter_detection",
  "input_modality": "audio",
  "output_format": "binary_classification",
  "labels": ["laugh", "no_laugh"],
  "training_data": "620_StandUp4AI_videos_weak_labels",
  "f1_estimate": 0.27,
  "iou_f1_estimate": 0.23,
  "downloads": 265,
  "key_findings": [
    "frozen_wavlm_can_detect_laughter_concept_proof",
    "simple_linear_probe_sufficient_for_baseline",
    "weak_labels_significantly_limit_accuracy"
  ],
  "use_cases": [
    "historical_reference",
    "baseline_benchmarking",
    "ml_education",
    "teaching_frozen_feature_probing"
  ],
  "limitations": [
    "weak_labels_vtt_markers",
    "low_f1_baseline",
    "linear_head_limited_capacity",
    "not_for_production_use_ten_instead"
  ]
}
```

---

<div align="center">

[![Hugging Face](https://img.shields.io/badge/🤗-Hugging%20Face-FFD21E?style=for-the-badge&logo=huggingface)](https://huggingface.co/Hayasuki/ChuckleNet-Genki)

*The spirit that started it all. 265+ downloads and counting.*

</div>

	---
	title: ChuckleNet-Genki — The Original Spirit
	emoji: 🎭
	colorFrom: blue
	colorTo: purple
	sdk: gradio
	pipeline_tag: audio-classification
	license: mit
	library_name: transformers
	base_model: microsoft/wavlm-base
	tags:
	- audio-classification
	- laughter-detection
	- wavlm
	- speech
	- humor
	- stand-up-comedy
	- transformers
	- genki
	metrics:
	- f1
	datasets:
	- StandUp4AI
	papers:
	- https://huggingface.co/Hayasuki/ChuckleNet-Genki
	---

	<div align="center">

	# 🎭 ChuckleNet-Genki (元気)

	*"The original spirit."*

	First-generation laughter detection model — the proof-of-concept that started it all.

	For production use, see [ChuckleNet-Ten](https://huggingface.co/Hayasuki/ChuckleNet-Ten) (human-verified, F1=0.975). For research, see [ChuckleNet-Kage](https://huggingface.co/Hayasuki/ChuckleNet-Kage) (IoU-F1=0.229).

	[![Downloads](https://img.shields.io/badge/Downloads-265+-brightgreen?style=flat-square)](https://huggingface.co/Hayasuki/ChuckleNet-Genki)
	[![License](https://img.shields.io/badge/License-MIT-green?style=flat-square)](https://opensource.org/licenses/MIT)
	[![Dataset](https://img.shields.io/badge/Dataset-StandUp4AI%20620v-purple?style=flat-square)](https://github.com/Standup4AI/dataset)

	Most-downloaded ChuckleNet model. Historical reference only — not for production.

	</div>

	---

	## Origin Story

	```
	┌─────────────────────────────────────────────────────────────────┐
	│ GENKI TIMELINE │
	├─────────────────────────────────────────────────────────────────┤
	│ │
	│ 2024-09 "Can frozen WavLM detect laughter?" │
	│ First experiment designed │
	│ │ │
	│ ▼ │
	│ 2024-10 Genki v1 trained: 620 StandUp4AI videos │
	│ Labels: VTT [laughter] transcript markers │
	│ Result: F1 = 0.27 — proof of concept! ✅ │
	│ │ │
	│ ▼ │
	│ 2025-01 "Weak labels are noisy — need human verification" │
	│ → ChuckleNet-Ten project begins │
	│ │ │
	│ ▼ │
	│ 2025-06 ChuckleNet-Ten: held-out IoU-F1 = 0.330 │
	│ (human-verified labels) │
	│ │
	│ Genki lives on as the historical baseline. │
	│ │
	└─────────────────────────────────────────────────────────────────┘
	```

	---

	## Model Characteristics

	\| Characteristic \| Description \|
	\|----------------\|-------------\|
	\| Generation \| First — v1 proof-of-concept \|
	\| Labels \| Weak — VTT `[laughter]` transcript markers \|
	\| Architecture \| Frozen WavLM (768-dim) → Linear (768→2) \|
	\| Training Data \| 620 StandUp4AI videos \|
	\| Status \| Historical reference only — not for production \|

	---

	## Performance

	\| Metric \| Value \| Note \|
	\|--------\|-------\|------\|
	\| F1 Score \| `~0.27` \| Weak-label metric, not human-verified \|
	\| Event IoU-F1@0.2 \| `~0.23` \| Approximate — different splits \|
	\| Downloads \| 265+ \| Most popular ChuckleNet model \|

	> ⚠️ These are weak-label results on VTT `[laughter]` markers — not ground-truth laughter events. The model detects patterns correlated with transcript markers, not actual audience laughter.

	---

	## Evolutionary Tree

	```
	ChuckleNet Family

	┌──────────────────┐
	│ microsoft/ │
	│ wavlm-base │
	│ (frozen) │
	└────────┬─────────┘
	│
	┌────────────────────┼────────────────────┐
	│ │ │
	▼ ▼ ▼
	┌───────────────┐ ┌───────────────┐ ┌───────────────┐
	│ 🎭 Genki v1 │ │ 👻 Kage v32 │ │ 🏔️ Ten │
	│ Weak labels │ │ Weak labels │ │ Human-verified│
	│ 620 videos │ │ 620 videos │ │ 87 videos │
	│ IoU ≈ 0.23 │ │ IoU = 0.229 │ │ IoU = 0.330 │
	│ First attempt │ │ Refined probe │ │ Gold standard │
	└───────────────┘ └───────────────┘ └───────────────┘
	2024-10 2025-03 2025-06
	```

	---

	## Comparison Table

	\| Model \| Labels \| Held-out IoU-F1 \| Downloads \| Use \|
	\|-------\|--------\|-----------------\|----------\|-----\|
	\| 🏔️ [ChuckleNet-Ten](https://huggingface.co/Hayasuki/ChuckleNet-Ten) \| Human-verified \| 0.330 \| New \| Production ✅ \|
	\| 👻 [ChuckleNet-Kage](https://huggingface.co/Hayasuki/ChuckleNet-Kage) \| Weak (VTT) \| 0.229 \| 49 \| Research \|
	\| 🎭 ChuckleNet-Genki \| Weak (VTT) \| ~0.23 \| 265+ \| Historical \|

	---

	## Quick Start

	```python
	from transformers import pipeline

	pipe = pipeline(
	"audio-classification",
	model="Hayasuki/ChuckleNet-Genki"
	)

	result = pipe("standup_clip.mp3")
	# [{'label': 'no_laugh', 'score': 0.77},
	# {'label': 'laugh', 'score': 0.23}]
	```

	> ⚠️ Genki is a baseline model with limited accuracy. For any real use case, use [ChuckleNet-Ten](https://huggingface.co/Hayasuki/ChuckleNet-Ten) instead.

	---

	## Teaching Use Case

	Genki is useful for ML education — demonstrating frozen feature probing:

	```python
	"""
	Genki for teaching: frozen feature probing concept
	"""

	import torch
	import torchaudio
	from transformers import WavLMModel

	# Load frozen WavLM backbone (NOT trained)
	wavlm = WavLMModel.from_pretrained("microsoft/wavlm-base")
	wavlm.eval() # Keep frozen

	# Genki's linear probe: 768 → 2
	class GenkiProbe(torch.nn.Module):
	def __init__(self, input_dim=768, num_classes=2):
	super().__init__()
	self.classifier = torch.nn.Linear(input_dim, num_classes)

	def forward(self, x):
	return self.classifier(x)

	# Load Genki weights (from this model card's files)
	probe = GenkiProbe()
	probe.load_state_dict(torch.load("genki_weights.pt"))

	# Extract frozen features
	with torch.no_grad():
	features = wavlm(audio).last_hidden_state
	# [batch, time, 768]

	# Temporal pooling
	pooled = features.mean(dim=1) # [batch, 768]

	# Classify with frozen features
	logits = probe(pooled) # [batch, 2]
	```

	---

	## Limitations

	\| Limitation \| Impact \|
	\|------------\|--------\|
	\| Weak labels only \| VTT markers ≠ actual laughter events \|
	\| Low F1 \| ~0.27 is a baseline, not production-ready \|
	\| Linear head \| No hidden layers — limited representation capacity \|
	\| Not for production \| Use [ChuckleNet-Ten](https://huggingface.co/Hayasuki/ChuckleNet-Ten) instead \|

	---

	## Resources

	\| Resource \| Link \|
	\|----------\|------\|
	\| Primary Model (Production) \| [ChuckleNet-Ten](https://huggingface.co/Hayasuki/ChuckleNet-Ten) \|
	\| Research Model \| [ChuckleNet-Kage](https://huggingface.co/Hayasuki/ChuckleNet-Kage) \|
	\| Dataset \| [StandUp4AI](https://github.com/Standup4AI/dataset) \|

	---

	## Citation

	```bibtex
	@misc{chuckleNetGenki2026,
	author = {Subhajit Das},
	title = {ChuckleNet-Genki: First Generation Laughter Detection},
	year = {2026},
	url = {https://huggingface.co/Hayasuki/ChuckleNet-Genki},
	note = {Historical baseline: first attempt at laughter detection using
	frozen WavLM on 620 StandUp4AI videos. Weak labels only.}
	}
	```

	---

	## Model Metadata (LLM-Parseable)

	```json
	{
	"model_name": "ChuckleNet-Genki",
	"model_name_zh": "笑音Net-魂",
	"architecture": "frozen_wavlm_base_plus_linear",
	"model_type": "first_generation_baseline",
	"primary_task": "audio_classification",
	"secondary_task": "laughter_detection",
	"input_modality": "audio",
	"output_format": "binary_classification",
	"labels": ["laugh", "no_laugh"],
	"training_data": "620_StandUp4AI_videos_weak_labels",
	"f1_estimate": 0.27,
	"iou_f1_estimate": 0.23,
	"downloads": 265,
	"key_findings": [
	"frozen_wavlm_can_detect_laughter_concept_proof",
	"simple_linear_probe_sufficient_for_baseline",
	"weak_labels_significantly_limit_accuracy"
	],
	"use_cases": [
	"historical_reference",
	"baseline_benchmarking",
	"ml_education",
	"teaching_frozen_feature_probing"
	],
	"limitations": [
	"weak_labels_vtt_markers",
	"low_f1_baseline",
	"linear_head_limited_capacity",
	"not_for_production_use_ten_instead"
	]
	}
	```

	---

	<div align="center">

	[![Hugging Face](https://img.shields.io/badge/🤗-Hugging%20Face-FFD21E?style=for-the-badge&logo=huggingface)](https://huggingface.co/Hayasuki/ChuckleNet-Genki)

	The spirit that started it all. 265+ downloads and counting.

	</div>