Create README.md

README.md

@@ -0,0 +1,120 @@
+---
+language:
+- ur
+license: apache-2.0
+tags:
+- automatic-speech-recognition
+- mozilla-foundation/common_voice_7_0
+- generated_from_trainer
+- ur
+- robust-speech-event
+- model_for_talk
+datasets:
+- mozilla-foundation/common_voice_7_0
+model-index:
+- name: XLS-R-300M - Urdu
+  results:
+  - task: 
+      name: Automatic Speech Recognition 
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 7
+      type: mozilla-foundation/common_voice_7_0
+      args: ur
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 105.66
+       - name: Test CER
+         type: cer
+         value: 434.011
+---
+
+<!-- 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. -->
+
+infinitejoy/wav2vec2-large-xls-r-300m-urdu
+
+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 - -UR 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-urdu --dataset speech-recognition-community-v2/dev_data \
+    --config ur --split validation --chunk_length_s 10 --stride_length_s 1
+```
+
+### Inference
+
+```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-urdu"
+
+sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "ur", 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):
+