| --- |
| license: apache-2.0 |
| datasets: |
| - ASCEND |
| language: |
| - zh |
| metrics: |
| - cer |
| tags: |
| - audio |
| - automatic-speech-recognition |
| - speech |
| - xlsr-fine-tuning-week |
| --- |
| |
|
|
| ## inference |
|
|
| The model can be used directly (without a language model) as follows... |
|
|
| Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: |
|
|
| ```python |
| from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC |
| from datasets import load_dataset |
| import torch |
| import torchaudio |
| |
| |
| |
| # load model and processor |
| processor = Wav2Vec2Processor.from_pretrained("gymeee/demo_code_switching") |
| model = Wav2Vec2ForCTC.from_pretrained("gymeee/demo_code_switching") |
| |
| # load speech |
| speech_array, sampling_rate = torchaudio.load("speech.wav") |
| # tokenize |
| input_values = processor(speech_array[0], return_tensors="pt", padding="longest").input_values # Batch size 1 |
| |
| # retrieve logits |
| logits = model(input_values).logits |
| |
| # take argmax and decode |
| predicted_ids = torch.argmax(logits, dim=-1) |
| transcription = processor.batch_decode(predicted_ids) |
| |
| transcription |
