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

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:

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.

import librosa
import soundfile as sf

def preprocess_sleep_audio(file_path, target_sr=16000):
    audio, sr = librosa.load(file_path, sr=target_sr)

    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

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.

from transformers import WhisperProcessor, WhisperForConditionalGeneration

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

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]

.

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 = 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.

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)

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. 💻✨