Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
 language: fa
 datasets:
-- common_voice_9_0
+- common_voice_6_1
 tags:
 - audio
 - automatic-speech-recognition
@@ -18,8 +18,8 @@ model-index:
       name: Automatic Speech Recognition
       type: automatic-speech-recognition
     dataset:
-      name: Common Voice Corpus 9.0 (clean)
-      type: common_voice_9_0
+      name: Common Voice Corpus 6.1 (clean)
+      type: common_voice_6_1
       config: clean
       split: test
       args: 
@@ -48,23 +48,23 @@ model-index:
 
 [Sharif-wav2vec2](https://huggingface.co/SLPL/Sharif-wav2vec2/)
 
-The base model fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
+The base model fine-tuned on 108 hours of Commonvoice on 16kHz sampled speech audio. When using the model
 make sure that your speech input is also sampled at 16Khz.
 
-[Paper](https://arxiv.org/abs/2006.11477)
+#[Paper](https://arxiv.org/abs/2006.11477)
 
-Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
+#Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
 
-**Abstract**
+#**Abstract**
 
-We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
+#We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can #outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and #solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all #labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec #2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of #labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech #recognition with limited amounts of labeled data.
 
 The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
 
 
 # Usage
 
-To transcribe audio files the model can be used as a standalone acoustic model as follows:
+To transcribe Persian audio files the model can be used as a standalone acoustic model as follows:
 
 ```python
  from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
@@ -72,11 +72,11 @@ To transcribe audio files the model can be used as a standalone acoustic model a
  import torch
  
  # load model and tokenizer
- processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
- model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+ processor = Wav2Vec2Processor.from_pretrained("SLPL/Sharif-wav2vec2")
+ model = Wav2Vec2ForCTC.from_pretrained("SLPL/Sharif-wav2vec2")
      
  # load dummy dataset and read soundfiles
- ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+# ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
  
  # tokenize
  input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values  # Batch size 1