Create README.md

README.md

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+---
+language:
+- ar
+license: apache-2.0
+tags:
+- automatic-speech-recognition
+- mozilla-foundation/common_voice_7_0
+- generated_from_trainer
+- ar
+- robust-speech-event
+- model_for_talk
+datasets:
+- mozilla-foundation/common_voice_7_0
+model-index:
+- name: XLS-R-300M - Arabic
+  results:
+  - task: 
+      name: Automatic Speech Recognition 
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 7
+      type: mozilla-foundation/common_voice_7_0
+      args: ar
+    metrics:
+       - name: Test WER
+         type: wer
+         value: NA
+       - name: Test CER
+         type: cer
+         value: NA
+  - task: 
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Robust Speech Event - Dev Data
+      type: speech-recognition-community-v2/dev_data
+      args: ar
+    metrics:
+       - name: Test WER
+         type: wer
+         value: NA
+       - name: Test CER
+         type: cer
+         value: NA
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# XLS-R-300m-SV
+
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AR dataset.
+It achieves the following results on the evaluation set:
+- Loss: NA
+- Wer: NA
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 7.5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 2000
+- num_epochs: 50.0
+- mixed_precision_training: Native AMP
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.16.0.dev0
+- Pytorch 1.10.0+cu102
+- Datasets 1.17.1.dev0
+- Tokenizers 0.10.3
+
+#### Evaluation Commands
+
+1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
+
+```bash
+python eval.py \
+    --model_id infinitejoy/wav2vec2-large-xls-r-300m-arabic \
+    --dataset mozilla-foundation/common_voice_7_0 --config ar --split test --log_outputs
+```
+
+2. To evaluate on `speech-recognition-community-v2/dev_data`
+
+```bash
+python eval.py \
+    --model_id infinitejoy/wav2vec2-large-xls-r-300m-arabic --dataset speech-recognition-community-v2/dev_data \
+    --config ar --split validation --chunk_length_s 10 --stride_length_s 1
+```
+
+### Inference With LM
+
+```python
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCTC, AutoProcessor
+import torchaudio.functional as F
+
+
+model_id = "infinitejoy/wav2vec2-large-xls-r-300m-arabic"
+
+sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "ar", split="test", streaming=True, use_auth_token=True))
+
+sample = next(sample_iter)
+resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
+
+model = AutoModelForCTC.from_pretrained(model_id)
+processor = AutoProcessor.from_pretrained(model_id)
+
+input_values = processor(resampled_audio, return_tensors="pt").input_values
+
+with torch.no_grad():
+    logits = model(input_values).logits
+
+transcription = processor.batch_decode(logits.numpy()).text
+
+```
+
+### Eval results on Common Voice 7 "test" (WER):
+
+| Without LM | With LM (run `./eval.py`) |
+|---|---|
+| NA | NA |
+