Update README.md

README.md

@@ -64,3 +64,38 @@ manually evaluate WER on test set - vietnamese part:
 | this LoRA | 14.7% | 14.7% | 9.4% |
 
 all training + evaluation scripts are on my repo: https://github.com/phineas-pta/fine-tune-whisper-vi
+
+usage example:
+```python
+# pip install peft accelerate bitsandbytes
+import torch
+import torchaudio
+from peft import PeftModel, PeftConfig
+from transformers import WhisperForConditionalGeneration, WhisperFeatureExtractor, WhisperTokenizer
+
+PEFT_MODEL_ID = "doof-ferb/whisper-large-peft-lora-vi"
+BASE_MODEL_ID = PeftConfig.from_pretrained(PEFT_MODEL_ID).base_model_name_or_path
+
+FEATURE_EXTRACTOR = WhisperFeatureExtractor.from_pretrained(BASE_MODEL_ID)
+TOKENIZER = WhisperTokenizer.from_pretrained(BASE_MODEL_ID)
+
+MODEL = PeftModel.from_pretrained(
+	WhisperForConditionalGeneration.from_pretrained(BASE_MODEL_ID, torch_dtype=torch.float16).to("cuda:0"),
+	PEFT_MODEL_ID
+).merge_and_unload(progressbar=True)
+
+DECODER_ID = torch.tensor(
+	TOKENIZER.convert_tokens_to_ids(["<|startoftranscript|>", "<|vi|>", "<|transcribe|>", "<|notimestamps|>"]),
+	device=MODEL.device
+).unsqueeze(dim=0)
+
+waveform, sampling_rate = torchaudio.load("audio.mp3")
+if waveform.size(0) > 1:  # convert dual to mono channel
+	waveform = waveform.mean(dim=0, keepdim=True)
+
+inputs = FEATURE_EXTRACTOR(waveform, sampling_rate=sampling_rate, return_tensors="pt").to(MODEL.device)
+with torch.inference_mode(), torch.autocast(device_type="cuda"):  # required by PEFT
+	predicted_ids = MODEL.generate(input_features=inputs.input_features, decoder_input_ids=DECODER_ID)
+
+TOKENIZER.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+```