# Snoring Secrets: Fine-Tuning Whisper-v3 to Identify Sleep Apnea Events Like a Pro

> Source: <https://dev.to/beck_moulton/snoring-secrets-fine-tuning-whisper-v3-to-identify-sleep-apnea-events-like-a-pro-4phj>
> Published: 2026-08-14 00:33:00+00:00

Have you ever wondered if that loud snoring is just a nuisance or a genuine health red flag? **Sleep Apnea detection** is traditionally done in uncomfortable sleep labs, but with the rise of **AI-powered sleep monitoring**, we can now turn a simple smartphone recording into a diagnostic-grade insight tool.

In this tutorial, we are diving deep into **Whisper-v3 audio processing**, leveraging **machine learning for health** to build a non-invasive acoustic monitor. By the end of this guide, you'll know how to take raw sleep audio, process it using **audio signal processing** techniques, and fine-tune OpenAI's Whisper-v3 to detect "Apnea" and "Hypopnea" events with high precision. 🚀

While Whisper is famous for speech-to-text, its architectural backbone is a robust encoder-decoder Transformer trained on diverse audio. By treating specific acoustic patterns (like the gasping or silence characteristic of Sleep Apnea) as "tokens" or specific classes, we can repurpose its timestamping capabilities to pinpoint exactly when a health event occurs.

To build this, we need a pipeline that handles everything from noise reduction to event classification. Here is how the data flows through our system:

``` php
graph TD
    A[Raw Sleep Audio .wav/.mp3] --> B{FFmpeg Preprocessing}
    B --> C[Librosa: Noise Reduction & Normalization]
    C --> D[Audio Slicing 30s Windows]
    D --> E[Whisper-v3 Feature Extractor]
    E --> F[Fine-tuned Whisper Encoder]
    F --> G[Timestamped Classification]
    G --> H[Apnea/Hypopnea Event Log]
    H --> I[Health Dashboard/Alerts]
```

Before we get our hands dirty, ensure you have the following stack ready:

`transformers`

or `openai-whisper`

)

```
pip install torch transformers librosa datasets evaluate jiwer
```

Sleep audio is notoriously "noisy." We need to filter out ambient fan noise while preserving the low-frequency rumbles of snoring. We'll use **Librosa** to convert the audio into a format Whisper loves: 16kHz mono.

``` python
import librosa
import soundfile as sf

def preprocess_sleep_audio(file_path, target_sr=16000):
    # Load audio and strip silence
    audio, sr = librosa.load(file_path, sr=target_sr)

    # Simple spectral subtraction for noise reduction
    stft = librosa.stft(audio)
    mag, phase = librosa.magphase(stft)
    noise_mag = np.mean(mag[:, :10], axis=1, keepdims=True)
    mag_clean = np.maximum(mag - 1.5 * noise_mag, 0)

    audio_clean = librosa.istft(mag_clean * phase)
    return audio_clean

# Example usage
cleaned_audio = preprocess_sleep_audio("bedroom_night_1.wav")
sf.write("cleaned_sample.wav", cleaned_audio, 16000)
```

Whisper expects a specific format. Since we aren't just transcribing words, we need to map acoustic events to labels. We use a custom `tokenizer`

approach where `<|apnea|>`

and `<|snore|>`

are added as special tokens.

``` python
from transformers import WhisperProcessor, WhisperForConditionalGeneration

model_id = "openai/whisper-v3"
processor = WhisperProcessor.from_pretrained(model_id)

# Adding special tokens for sleep events
special_tokens_dict = {"additional_special_tokens": ["<|apnea|>", "<|hypopnea|>", "<|snore|>"]}
processor.tokenizer.add_special_tokens(special_tokens_dict)

model = WhisperForConditionalGeneration.from_pretrained(model_id)
model.resize_token_embeddings(len(processor.tokenizer))
```

We utilize the `Seq2SeqTrainer`

from Hugging Face. The goal is to feed the model a 30-second Mel Spectrogram and expect it to output a sequence like: `[00:05.00] <|snore|> [00:12.00] <|apnea|> [00:22.00]`

.

``` python
from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer

training_args = Seq2SeqTrainingArguments(
    output_dir="./whisper-sleep-apnea",
    per_device_train_batch_size=8,
    gradient_accumulation_steps=2,
    learning_rate=1e-5,
    warmup_steps=500,
    max_steps=5000,
    fp16=True,
    evaluation_strategy="steps",
    per_device_eval_batch_size=8,
    predict_with_generate=True,
    generation_max_length=225,
    save_steps=1000,
    eval_steps=1000,
    logging_steps=25,
    report_to=["tensorboard"],
)

# Trainer initialization (Assuming 'common_voice' style dataset format)
trainer = Seq2SeqTrainer(
    args=training_args,
    model=model,
    train_dataset=my_sleep_data["train"],
    eval_dataset=my_sleep_data["test"],
    data_collator=data_collator,
    tokenizer=processor.feature_extractor,
)

trainer.train()
```

When moving from a notebook to a production-ready edge device, you'll need to optimize for latency. Running a full Whisper-v3 model on a smartphone or a small Raspberry Pi requires quantization (INT8) or using a distilled version.

For those looking to dive deeper into **production-grade AI deployment** and advanced signal processing patterns, I highly recommend checking out the technical deep-dives over at ** WellAlly Tech Blog**. They have some fantastic resources on scaling audio models and optimizing inference for real-time monitoring.

Once trained, we can run the model on a full night's recording using a sliding window. We then visualize the "Oxygen Desaturation" risk based on the density of apnea events.

``` python
import torch
from transformers import pipeline

device = "cuda:0" if torch.cuda.is_available() else "cpu"
pipe = pipeline("automatic-speech-recognition", model=model, device=device)

# Running inference on a 30s segment
result = pipe("test_segment.wav", generate_kwargs={"task": "transcribe"})
print(f"Detected Events: {result['text']}")
```

A typical output would show a "heat map" of breathing interruptions over an 8-hour period, allowing users to see if their apnea events cluster during REM sleep.

Turning raw audio into life-saving data is the superpower of modern AI. By fine-tuning **Whisper-v3**, we transition from simple transcription to sophisticated **acoustic event detection**. This non-invasive approach lowers the barrier to entry for sleep health, making it accessible to anyone with a microphone.

**What's next?**

Are you building something in the health-tech space? Drop a comment below or share your results! And don't forget to visit ** wellally.tech/blog** for more advanced AI tutorials. 💻✨