Update README.md

README.md

@@ -1,320 +1,319 @@
-Fine-tuned Wav2Vec2 on Hindi using the following datasets:
-
-- [Common Voice](https://huggingface.co/datasets/common_voice),
-
-- [Indic TTS- IITM](https://www.iitm.ac.in/donlab/tts/index.php) and
-
-- [IIITH - Indic Speech Datasets](http://speech.iiit.ac.in/index.php/research-svl/69.html)
-
-The Indic datasets are well balanced across gender and accents. However the CommonVoice dataset is skewed towards male voices
-
-Fine-tuned on Wav2Vec2 using Hindi dataset :: 60 epochs >> 17.05% WER
-
-When using this model, make sure that your speech input is sampled at 16kHz.
-
-## Usage
-
-The model can be used directly (without a language model) as follows:
-
-```python
-
-import torch
-
-import torchaudio
-
-from datasets import load_dataset
-
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
-
-test_dataset = load_dataset("common_voice", "hi", split="test")
-
-
-
-processor = Wav2Vec2Processor.from_pretrained("Maverick1713/Hindi-ASR")
-
-model = Wav2Vec2ForCTC.from_pretrained("Maverick1713/Hindi-ASR")
-
-
-
-resampler = torchaudio.transforms.Resample(48_000, 16_000)
-
-
-
-
-def speech_file_to_array_fn(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)
-
-
-
-with torch.no_grad():
-
-  logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
-
-
-
-predicted_ids = torch.argmax(logits, dim=-1)
-
-print("Prediction:", processor.batch_decode(predicted_ids))
-
-print("Reference:", test_dataset["sentence"][:2])
-
-```
-
-## Predictions
-
-_Some good ones ..... _
-
-| Predictions | Reference |
-
-|-------|-------|
-
-|फिर वो सूरज तारे पहाड बारिश पदछड़ दिन रात शाम नदी बर्फ़ समुद्र धुंध हवा कुछ भी हो सकती है | फिर वो सूरज तारे पहाड़ बारिश पतझड़ दिन रात शाम नदी बर्फ़ समुद्र धुंध हवा कुछ भी हो सकती है |
-
-| इस कारण जंगल में बडी दूर स्थित राघव के आश्रम में लोघ कम आने लगे और अधिकांश भक्त सुंदर के आश्रम में जाने लगे | इस कारण जंगल में बड़ी दूर स्थित राघव के आश्रम में लोग कम आने लगे और अधिकांश भक्त सुन्दर के आश्रम में जाने लगे |
-
-| अपने बचन के अनुसार शुभमूर्त पर अनंत दक्षिणी पर्वत गया और मंत्रों का जप करके सरोवर में उतरा | अपने बचन के अनुसार शुभमुहूर्त पर अनंत दक्षिणी पर्वत गया और मंत्रों का जप करके सरोवर में उतरा |
-
-_Some crappy stuff .... _
-
-| Predictions | Reference |
-
-|-------|-------|
-
-| वस गनिल साफ़ है। | उसका दिल साफ़ है। |
-
-| चाय वा एक कुछ लैंगे हब | चायवाय कुछ लेंगे आप |
-
-| टॉम आधे है स्कूल हें है | टॉम अभी भी स्कूल में है |
-
-## Evaluation
-
-The model can be evaluated as follows on the following two datasets:
-
-1. Custom dataset created from 20% of Indic, IIITH and CV (test): WER 17.xx%
-
-2. CommonVoice Hindi test dataset: WER 56.xx%
-
-Links to the datasets are provided above (check the links at the start of the README)
-
-train-test csv files are shared on the following gdrive links:
-
-a. IIITH [train](https://storage.googleapis.com/indic-dataset/train_test_splits/iiit_hi_train.csv) [test](https://storage.googleapis.com/indic-dataset/train_test_splits/iiit_hi_test.csv)
-
-b. Indic TTS [train](https://storage.googleapis.com/indic-dataset/train_test_splits/indic_train_full.csv) [test](https://storage.googleapis.com/indic-dataset/train_test_splits/indic_test_full.csv)
-
-Update the audio_path as per your local file structure.
-
-```python
-
-import torch
-
-import torchaudio
-
-from datasets import load_dataset, load_metric
-
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
-
-import re
-
-
-
-
-test_dataset = load_dataset("common_voice", "hi", split="test")
-
-
-
-indic = load_dataset("csv", data_files= {'train':"/workspace/data/hi2/indic_train_full.csv",
-
-                                        "test": "/workspace/data/hi2/indic_test_full.csv"}, download_mode="force_redownload")
-
-iiith = load_dataset("csv", data_files= {"train": "/workspace/data/hi2/iiit_hi_train.csv",
-
-                                        "test": "/workspace/data/hi2/iiit_hi_test.csv"}, download_mode="force_redownload")
-
-
-
-split = ['train', 'test', 'validation', 'other', 'invalidated']
-
-
-
-for sp in split:
-
-    common_voice[sp] = common_voice[sp].remove_columns(['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'])
-
-
-
-common_voice = common_voice.rename_column('path', 'audio_path')
-
-common_voice = common_voice.rename_column('sentence', 'target_text')
-
-
-
-train_dataset = datasets.concatenate_datasets([indic['train'], iiith['train'], common_voice['train']])
-
-test_dataset = datasets.concatenate_datasets([indic['test'], iiith['test'], common_voice['test'], common_voice['validation']])
-
-
-
-
-
-wer = load_metric("wer")
-
-
-
-processor = Wav2Vec2Processor.from_pretrained("Maverick1713/Hindi-ASR")
-
-model = Wav2Vec2ForCTC.from_pretrained("Maverick1713/Hindi-ASR")
-
-model.to("cuda")
-
-
-
-chars_to_ignore_regex = '[\,\?\.\!\-\'\;\:\"\“\%\‘\”\�Utrnle\_]'
-
-unicode_ignore_regex = r'[dceMaWpmFui\xa0\u200d]' # Some unwanted unicode chars
-
-resampler = torchaudio.transforms.Resample(48_000, 16_000)
-
-
-
-
-
-def speech_file_to_array_fn(batch):
-
-  batch["target_text"] = re.sub(chars_to_ignore_regex, '', batch["target_text"])
-
-  batch["target_text"] = re.sub(unicode_ignore_regex, '', batch["target_text"])
-
-
-
-  speech_array, sampling_rate = torchaudio.load(batch["audio_path"])
-
-  batch["speech"] = resampler(speech_array).squeeze().numpy()
-
-  return batch
-
-
-
-test_dataset = test_dataset.map(speech_file_to_array_fn)
-
-
-
-
-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
-
-  pred_ids = torch.argmax(logits, dim=-1)
-
-  batch["pred_strings"] = processor.batch_decode(pred_ids)
-
-  return batch
-
-
-
-result = test_dataset.map(evaluate, batched=True, batch_size=8)
-
-print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
-
-```
-
-**Test Result on custom dataset**: 17.23 %
-
-```python
-
-import torch
-
-import torchaudio
-
-from datasets import load_dataset, load_metric
-
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
-
-import re
-
-
-
-test_dataset = load_dataset("common_voice", "hi", split="test")
-
-wer = load_metric("wer")
-
-
-
-processor = Wav2Vec2Processor.from_pretrained("Maverick1713/Hindi-ASR")
-
-model = Wav2Vec2ForCTC.from_pretrained("Maverick1713/Hindi-ASR")
-
-model.to("cuda")
-
-
-
-chars_to_ignore_regex = '[\,\?\.\!\-\'\;\:\"\“\%\‘\”\�Utrnle\_]'
-
-unicode_ignore_regex = r'[dceMaWpmFui\xa0\u200d]'
-
-resampler = torchaudio.transforms.Resample(48_000, 16_000)
-
-
-
-
-
-def speech_file_to_array_fn(batch):
-
-  batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).sub(unicode_ignore_regex, '', batch["sentence"])
-
-  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)
-
-
-
-
-
-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
-
-  pred_ids = torch.argmax(logits, dim=-1)
-
-  batch["pred_strings"] = processor.batch_decode(pred_ids)
-
-  return batch
-
-
-
-result = test_dataset.map(evaluate, batched=True, batch_size=8)
-
-print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
-
-```
-
-**Test Result on CommonVoice**: 56.46 %
-
-## Training
-
-The Common Voice `train`, `validation`, datasets were used for training as well as
+Fine-tuned Wav2Vec2 on Hindi using the following datasets:
+
+- [Common Voice](https://huggingface.co/datasets/common_voice),
+
+- [Indic TTS- IITM](https://www.iitm.ac.in/donlab/tts/index.php) and
+
+
+The Indic datasets are well balanced across gender and accents. However the CommonVoice dataset is skewed towards male voices
+
+Fine-tuned on Wav2Vec2 using Hindi dataset :: 60 epochs >> 17.05% WER
+
+When using this model, make sure that your speech input is sampled at 16kHz.
+
+## Usage
+
+The model can be used directly (without a language model) as follows:
+
+```python
+
+import torch
+
+import torchaudio
+
+from datasets import load_dataset
+
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+test_dataset = load_dataset("common_voice", "hi", split="test")
+
+
+
+processor = Wav2Vec2Processor.from_pretrained("Maverick1713/Hindi-ASR")
+
+model = Wav2Vec2ForCTC.from_pretrained("Maverick1713/Hindi-ASR")
+
+
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+
+
+
+def speech_file_to_array_fn(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)
+
+
+
+with torch.no_grad():
+
+  logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+
+
+
+predicted_ids = torch.argmax(logits, dim=-1)
+
+print("Prediction:", processor.batch_decode(predicted_ids))
+
+print("Reference:", test_dataset["sentence"][:2])
+
+```
+
+## Predictions
+
+_Some good ones ..... _
+
+| Predictions | Reference |
+
+|-------|-------|
+
+|फिर वो सूरज तारे पहाड बारिश पदछड़ दिन रात शाम नदी बर्फ़ समुद्र धुंध हवा कुछ भी हो सकती है | फिर वो सूरज तारे पहाड़ बारिश पतझड़ दिन रात शाम नदी बर्फ़ समुद्र धुंध हवा कुछ भी हो सकती है |
+
+| इस कारण जंगल में बडी दूर स्थित राघव के आश्रम में लोघ कम आने लगे और अधिकांश भक्त सुंदर के आश्रम में जाने लगे | इस कारण जंगल में बड़ी दूर स्थित राघव के आश्रम में लोग कम आने लगे और अधिकांश भक्त सुन्दर के आश्रम में जाने लगे |
+
+| अपने बचन के अनुसार शुभमूर्त पर अनंत दक्षिणी पर्वत गया और मंत्रों का जप करके सरोवर में उतरा | अपने बचन के अनुसार शुभमुहूर्त पर अनंत दक्षिणी पर्वत गया और मंत्रों का जप करके सरोवर में उतरा |
+
+_Some crappy stuff .... _
+
+| Predictions | Reference |
+
+|-------|-------|
+
+| वस गनिल साफ़ है। | उसका दिल साफ़ है। |
+
+| चाय वा एक कुछ लैंगे हब | चायवाय कुछ लेंगे आप |
+
+| टॉम आधे है स्कूल हें है | टॉम अभी भी स्कूल में है |
+
+## Evaluation
+
+The model can be evaluated as follows on the following two datasets:
+
+1. Custom dataset created from 20% of Indic, IIITH and CV (test): WER 17.xx%
+
+2. CommonVoice Hindi test dataset: WER 56.xx%
+
+Links to the datasets are provided above (check the links at the start of the README)
+
+train-test csv files are shared on the following gdrive links:
+
+a. IIITH [train](https://storage.googleapis.com/indic-dataset/train_test_splits/iiit_hi_train.csv) [test](https://storage.googleapis.com/indic-dataset/train_test_splits/iiit_hi_test.csv)
+
+b. Indic TTS [train](https://storage.googleapis.com/indic-dataset/train_test_splits/indic_train_full.csv) [test](https://storage.googleapis.com/indic-dataset/train_test_splits/indic_test_full.csv)
+
+Update the audio_path as per your local file structure.
+
+```python
+
+import torch
+
+import torchaudio
+
+from datasets import load_dataset, load_metric
+
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+import re
+
+
+
+
+test_dataset = load_dataset("common_voice", "hi", split="test")
+
+
+
+indic = load_dataset("csv", data_files= {'train':"/workspace/data/hi2/indic_train_full.csv",
+
+                                        "test": "/workspace/data/hi2/indic_test_full.csv"}, download_mode="force_redownload")
+
+iiith = load_dataset("csv", data_files= {"train": "/workspace/data/hi2/iiit_hi_train.csv",
+
+                                        "test": "/workspace/data/hi2/iiit_hi_test.csv"}, download_mode="force_redownload")
+
+
+
+split = ['train', 'test', 'validation', 'other', 'invalidated']
+
+
+
+for sp in split:
+
+    common_voice[sp] = common_voice[sp].remove_columns(['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'])
+
+
+
+common_voice = common_voice.rename_column('path', 'audio_path')
+
+common_voice = common_voice.rename_column('sentence', 'target_text')
+
+
+
+train_dataset = datasets.concatenate_datasets([indic['train'], iiith['train'], common_voice['train']])
+
+test_dataset = datasets.concatenate_datasets([indic['test'], iiith['test'], common_voice['test'], common_voice['validation']])
+
+
+
+
+
+wer = load_metric("wer")
+
+
+
+processor = Wav2Vec2Processor.from_pretrained("Maverick1713/Hindi-ASR")
+
+model = Wav2Vec2ForCTC.from_pretrained("Maverick1713/Hindi-ASR")
+
+model.to("cuda")
+
+
+
+chars_to_ignore_regex = '[\,\?\.\!\-\'\;\:\"\“\%\‘\”\�Utrnle\_]'
+
+unicode_ignore_regex = r'[dceMaWpmFui\xa0\u200d]' # Some unwanted unicode chars
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+
+
+
+
+def speech_file_to_array_fn(batch):
+
+  batch["target_text"] = re.sub(chars_to_ignore_regex, '', batch["target_text"])
+
+  batch["target_text"] = re.sub(unicode_ignore_regex, '', batch["target_text"])
+
+
+
+  speech_array, sampling_rate = torchaudio.load(batch["audio_path"])
+
+  batch["speech"] = resampler(speech_array).squeeze().numpy()
+
+  return batch
+
+
+
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+
+
+
+
+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
+
+  pred_ids = torch.argmax(logits, dim=-1)
+
+  batch["pred_strings"] = processor.batch_decode(pred_ids)
+
+  return batch
+
+
+
+result = test_dataset.map(evaluate, batched=True, batch_size=8)
+
+print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
+
+```
+
+**Test Result on custom dataset**: 17.23 %
+
+```python
+
+import torch
+
+import torchaudio
+
+from datasets import load_dataset, load_metric
+
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+import re
+
+
+
+test_dataset = load_dataset("common_voice", "hi", split="test")
+
+wer = load_metric("wer")
+
+
+
+processor = Wav2Vec2Processor.from_pretrained("Maverick1713/Hindi-ASR")
+
+model = Wav2Vec2ForCTC.from_pretrained("Maverick1713/Hindi-ASR")
+
+model.to("cuda")
+
+
+
+chars_to_ignore_regex = '[\,\?\.\!\-\'\;\:\"\“\%\‘\”\�Utrnle\_]'
+
+unicode_ignore_regex = r'[dceMaWpmFui\xa0\u200d]'
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+
+
+
+
+def speech_file_to_array_fn(batch):
+
+  batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).sub(unicode_ignore_regex, '', batch["sentence"])
+
+  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)
+
+
+
+
+
+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
+
+  pred_ids = torch.argmax(logits, dim=-1)
+
+  batch["pred_strings"] = processor.batch_decode(pred_ids)
+
+  return batch
+
+
+
+result = test_dataset.map(evaluate, batched=True, batch_size=8)
+
+print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
+
+```
+
+**Test Result on CommonVoice**: 56.46 %
+
+## Training
+
+The Common Voice `train`, `validation`, datasets were used for training as well as