| import torch | |
| import librosa | |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
| # ---------------------------- | |
| # CONFIG | |
| # ---------------------------- | |
| CHECKPOINT_PATH = "./wav2vec2-xlsr-khmer-300m/checkpoint-1800" | |
| AUDIO_PATH = "data\wavs\00000.wav" # <-- change this to your test file | |
| # ---------------------------- | |
| # LOAD MODEL AND PROCESSOR | |
| # ---------------------------- | |
| print("Loading model and processor...") | |
| processor = Wav2Vec2Processor.from_pretrained(CHECKPOINT_PATH) | |
| model = Wav2Vec2ForCTC.from_pretrained(CHECKPOINT_PATH) | |
| model.eval() | |
| # ---------------------------- | |
| # LOAD AUDIO | |
| # ---------------------------- | |
| print("Loading audio:", AUDIO_PATH) | |
| speech, sr = librosa.load(AUDIO_PATH, sr=16000) | |
| inputs = processor( | |
| speech, | |
| sampling_rate=16000, | |
| return_tensors="pt", | |
| padding=True | |
| ) | |
| with torch.no_grad(): | |
| logits = model(inputs.input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcription = processor.batch_decode(predicted_ids)[0] | |
| print("\n===============================") | |
| print("🔊 Transcription Result:") | |
| print(transcription) | |
| print("===============================") | |