step5_wavlm_test.py

import torch
import librosa
from transformers import AutoFeatureExtractor, AutoModel

MODEL_ID = "microsoft/wavlm-base"

def load_audio(path: str, target_sr: int = 16000):
    audio, sr = librosa.load(path, sr=target_sr, mono=True)
    return audio, sr

def main():
    print("Loading model:", MODEL_ID)

    feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_ID)
    model = AutoModel.from_pretrained(MODEL_ID)
    model.eval()

    audio, sr = load_audio("sample.wav")
    print("Audio length (sec):", round(len(audio) / sr, 2))

    inputs = feature_extractor(audio, sampling_rate=sr, return_tensors="pt")

    with torch.no_grad():
        out = model(**inputs)

    x = out.last_hidden_state  # [batch, frames, hidden]
    print("OK ✅ WavLM ran on CPU")
    print("Embedding tensor shape:", tuple(x.shape))

if __name__ == "__main__":
    main()