Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
-language: ta
+language: Guj
 datasets:
-- common_voice
+- google
 tags:
 - audio
 - automatic-speech-recognition
@@ -9,24 +9,24 @@ tags:
 - xlsr-fine-tuning-week
 license: apache-2.0
 model-index:
-- name: XLSR Wav2Vec2 Tamil by Amrrs
+- name: XLSR Wav2Vec2 Guj by Jaimin
   results:
   - task: 
       name: Speech Recognition
       type: automatic-speech-recognition
     dataset:
-      name: Common Voice ta
-      type: common_voice
-      args: ta  
+      name: Google
+      type: voice
+      args: guj
     metrics:
        - name: Test WER
          type: wer
-         value: 82.94
+         value: 28.92
 ---
 
 # Wav2Vec2-Large-XLSR-53-Tamil
 
-Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Tamil using the [Common Voice](https://huggingface.co/datasets/common_voice)
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Guj
 When using this model, make sure that your speech input is sampled at 16kHz.
 
 ## Usage
@@ -39,30 +39,30 @@ import torchaudio
 from datasets import load_dataset
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
-test_dataset = load_dataset("common_voice", "ta", split="test[:2%]").
+common_voice_train,common_voice_test = load_dataset('csv', data_files={'train': 'train.csv','test': 'test.csv'},error_bad_lines=False,encoding='utf-8',split=['train', 'test']).
 
-processor = Wav2Vec2Processor.from_pretrained("Amrrs/wav2vec2-large-xlsr-53-tamil")
-model = Wav2Vec2ForCTC.from_pretrained("Amrrs/wav2vec2-large-xlsr-53-tamil")
+processor = Wav2Vec2Processor.from_pretrained("jaimin/wav2vec2-base-gujarati-demo")
+model = Wav2Vec2ForCTC.from_pretrained("jaimin/wav2vec2-base-gujarati-demo")
 
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
 
 # Preprocessing the datasets.
 # We need to read the aduio files as arrays
 def speech_file_to_array_fn(batch):
-	speech_array, sampling_rate = torchaudio.load(batch["path"])
-	batch["speech"] = resampler(speech_array).squeeze().numpy()
-	return batch
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    batch["speech"] = resampler(speech_array).squeeze().numpy()
+    return batch
 
-test_dataset = test_dataset.map(speech_file_to_array_fn)
-inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
+test_dataset = common_voice_test.map(speech_file_to_array_fn)
+inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
 
 with torch.no_grad():
-	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+     logits = model(inputs.input_values).logits
 
 predicted_ids = torch.argmax(logits, dim=-1)
 
 print("Prediction:", processor.batch_decode(predicted_ids))
-print("Reference:", test_dataset["sentence"][:2])
+print("Reference:", test_dataset["sentence"][0].lower())
 ```
 
 
@@ -78,11 +78,11 @@ from datasets import load_dataset, load_metric
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 import re
 
-test_dataset = load_dataset("common_voice", "ta", split="test")
+common_voice_validation = load_dataset('csv', data_files={'test': 'validation.csv'},error_bad_lines=False,encoding='utf-8',split='test')
 wer = load_metric("wer")
 
-processor = Wav2Vec2Processor.from_pretrained("Amrrs/wav2vec2-large-xlsr-53-tamil")
-model = Wav2Vec2ForCTC.from_pretrained("Amrrs/wav2vec2-large-xlsr-53-tamil")
+processor = Wav2Vec2Processor.from_pretrained("jaimin/wav2vec2-base-gujarati-demo")
+model = Wav2Vec2ForCTC.from_pretrained("Amrrs/wav2vec2-base-gujarati-demo")
 model.to("cuda")
 
 chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
@@ -91,35 +91,35 @@ resampler = torchaudio.transforms.Resample(48_000, 16_000)
 # Preprocessing the datasets.
 # We need to read the aduio files as arrays
 def speech_file_to_array_fn(batch):
-	batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
-	speech_array, sampling_rate = torchaudio.load(batch["path"])
-	batch["speech"] = resampler(speech_array).squeeze().numpy()
-	return batch
+    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    batch["speech"] = resampler(speech_array).squeeze().numpy()
+    return batch
 
-test_dataset = test_dataset.map(speech_file_to_array_fn)
+test_dataset = common_voice_validation.map(speech_file_to_array_fn)
 
 # Preprocessing the datasets.
 # We need to read the aduio files as arrays
 def evaluate(batch):
-	inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
-
-	with torch.no_grad():
-		logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
+    inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding=True)
 
+    with torch.no_grad():
+        logits = model(inputs.input_values.to("cuda")).logits
+    
     pred_ids = torch.argmax(logits, dim=-1)
-	batch["pred_strings"] = processor.batch_decode(pred_ids)
-	return batch
+    batch["pred_strings"] = processor.batch_decode(pred_ids)
+    return batch
 
-result = test_dataset.map(evaluate, batched=True, batch_size=8)
+result = common_voice_validation.map(evaluate, batched=True, batch_size=8)
 
 print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
 ```
 
-**Test Result**: 82.94 %
+**Test Result**: 28.92 %
 
 
 ## Training
 
-The Common Voice `train`, `validation` datasets were used for training.
+The Google datasets were used for training.
 
 The script used for training can be found [here](https://colab.research.google.com/drive/1-Klkgr4f-C9SanHfVC5RhP0ELUH6TYlN?usp=sharing)