README.md

README.md

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+---
+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)
+
+print(transcription)