add the model card

README.md

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+---
+license: cc-by-4.0
+language: tr
+tags:
+- automatic-speech-recognition
+- common_voice
+- tr
+- robust-speech-event
+datasets:
+- common_voice
+model-index:
+- name: mpoyraz/wav2vec2-xls-r-300m-cv6-turkish
+  results:
+  - task: 
+       name: Automatic Speech Recognition 
+       type: automatic-speech-recognition
+    dataset:
+       name: Common Voice 6.1
+       type: common_voice
+       args: tr
+    metrics:
+      - name: Test WER
+        type: wer
+        value: 8.83
+      - name: Test CER
+        type: cer
+        value: 2.37
+  - task: 
+       name: Automatic Speech Recognition
+       type: automatic-speech-recognition
+    dataset:
+       name: Robust Speech Event - Dev Data
+       type: speech-recognition-community-v2/dev_data
+       args: tr
+    metrics:
+      - name: Test WER
+        type: wer
+        value: 32.81
+      - name: Test CER
+        type: cer
+        value: 11.22
+---
+
+# wav2vec2-xls-r-300m-cv6-turkish
+
+## Model description
+This ASR model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Turkish language.
+
+## Training and evaluation data
+The following datasets were used for finetuning:
+ - [Common Voice 6.1 TR](https://huggingface.co/datasets/common_voice) All `validated` split except `test` split was used for training.
+ - [MediaSpeech](https://www.openslr.org/108/)
+
+## Training procedure
+To support both of the datasets above, custom pre-processing and loading steps was performed and [wav2vec2-turkish](https://github.com/mpoyraz/wav2vec2-turkish) repo was used for that purpose.
+
+### Training hyperparameters
+The following hypermaters were used for finetuning:
+- learning_rate 2e-4
+- num_train_epochs 10
+- warmup_steps 500
+- freeze_feature_extractor
+- mask_time_prob 0.1
+- mask_feature_prob 0.1
+- feat_proj_dropout 0.05
+- attention_dropout 0.05
+- final_dropout 0.1
+- activation_dropout 0.05
+- per_device_train_batch_size 8
+- per_device_eval_batch_size 8
+- gradient_accumulation_steps 8
+
+### Framework versions
+- Transformers 4.17.0.dev0
+- Pytorch 1.10.1
+- Datasets 1.18.3
+- Tokenizers 0.10.3
+
+## Language Model
+N-gram language model is trained on a Turkish Wikipedia articles using KenLM and [ngram-lm-wiki](https://github.com/mpoyraz/ngram-lm-wiki) repo was used to generate arpa LM and convert it into binary format.
+
+## Evaluation Commands
+Please install [unicode_tr](https://pypi.org/project/unicode_tr/) package before running evaluation. It is used for Turkish text processing.
+1. To evaluate on `common_voice` with split `test`
+```bash
+python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv6-turkish --dataset common_voice --config tr --split test
+```
+
+2. To evaluate on `speech-recognition-community-v2/dev_data`
+
+```bash
+python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv6-turkish --dataset speech-recognition-community-v2/dev_data --config tr --split validation --chunk_length_s 5.0 --stride_length_s 1.0
+```
+## Evaluation results:
+
+| Dataset | WER | CER |
+|---|---|---|
+|Common Voice 6.1 TR test split| 8.83 | 2.37 |
+|Speech Recognition Community dev data| 32.81 | 11.22 |