Create README.md

README.md

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+---
+language: ary
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: XLSR Wav2Vec2 Moroccan Arabic dialect by Boumehdi
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 0.496
+---
+# Wav2Vec2-Large-XLSR-53-Moroccan
+
+Fine-tuned [othrif/wav2vec2-large-xlsr-moroccan](https://huggingface.co/othrif/wav2vec2-large-xlsr-moroccan) on 6 hours of labelled speech
+
+## Usage
+
+The model can be used directly (without a language model) as follows:
+
+```python
+import librosa
+import torch
+from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2ForCTC, Wav2Vec2Processor, TrainingArguments, Wav2Vec2FeatureExtractor, Trainer
+
+tokenizer = Wav2Vec2CTCTokenizer("./vocab.json", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|")
+processor = Wav2Vec2Processor.from_pretrained('boumehdi/wav2vec2-large-xlsr-moroccan-darija-v1', tokenizer=tokenizer)
+model=Wav2Vec2ForCTC.from_pretrained('boumehdi/wav2vec2-large-xlsr-moroccan-darija-v1')
+
+
+# load the audio data (use your own wav file here!)
+input_audio, sr = librosa.load('file.wav', sr=16000)
+
+# tokenize
+input_values = processor(input_audio, return_tensors="pt", padding=True).input_values
+
+# retrieve logits
+logits = model(input_values).logits
+
+tokens=torch.argmax(logits, axis=-1)
+
+# decode using n-gram
+transcription = tokenizer.batch_decode(tokens)
+
+# print the output
+print(transcription)
+```
+
+## Evaluation
+
+
+**Test Result**: 49.68