Add model files

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,142 @@
+---
+language: hi
+datasets:
+- common_voice 
+- indic tts
+- iiith 
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: Hindi XLSR Wav2Vec2 Large 53
+results:
+- task: 
+    name: Speech Recognition
+    type: automatic-speech-recognition
+    dataset:
+      - name: Common Voice hi
+        type: common_voice
+        args: hi 
+      - name: Indic IIT (IITM)
+        type: indic
+        args: hi
+      - name: IIITH Indic Dataset
+        type: iiith
+        args: hi
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 19.05
+---
+
+# Wav2Vec2-Large-XLSR-53-Hindi
+
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) 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 Hindi CommonVoice data is skewed towards male voices. However the other Indic datasets are well balanced. 
+
+Fine-tuned on facebook/wav2vec2-large-xlsr-53 using Hindi dataset :: 30 epochs >> 19.05% WER
+                          Resuming from checkpoints trained for another XX epochs >> XX.XX%
+
+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("skylord/wav2vec2-large-xlsr-hindi") 
+model = Wav2Vec2ForCTC.from_pretrained("skylord/wav2vec2-large-xlsr-hindi") 
+
+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
+
+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])
+```
+
+
+## Evaluation
+
+The model can be evaluated as follows on the Hindi test data of Common Voice. 
+
+
+```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("skylord/wav2vec2-large-xlsr-hindi") 
+model = Wav2Vec2ForCTC.from_pretrained("skylord/wav2vec2-large-xlsr-hindi")
+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)
+
+# 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"]).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)
+
+# 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
+  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**: 19.056 % 
+
+
+## Training
+
+The Common Voice `train`, `validation`, datasets were used for training as well as   
+
+The script used for training & wandb dashboard can be found [here](https://wandb.ai/thinkevolve/huggingface/reports/Project-Hindi-XLSR-Large--Vmlldzo2MTI2MTQ)

README.md

@@ -95,7 +95,7 @@ from datasets import load_dataset, load_metric
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 import re
 
-test_dataset = load_dataset("common_voice", "el", split="test") 
+test_dataset = load_dataset("common_voice", "hi", split="test") 
 wer = load_metric("wer")
 
 processor = Wav2Vec2Processor.from_pretrained("skylord/wav2vec2-large-xlsr-hindi") 
@@ -103,7 +103,7 @@ model = Wav2Vec2ForCTC.from_pretrained("skylord/wav2vec2-large-xlsr-hindi")
 model.to("cuda")
 
 chars_to_ignore_regex = '[\,\?\.\!\-\'\;\:\"\“\%\‘\”\�Utrnle\_]'
-unicode_ignore_regex = r'[dceMaWpmFui\xa0]' # Some unwanted unicode chars
+unicode_ignore_regex = r'[dceMaWpmFui\xa0\u200d]' # Some unwanted unicode chars
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
 
 # Preprocessing the datasets.

config.json

@@ -70,7 +70,7 @@
   "num_conv_pos_embeddings": 128,
   "num_feat_extract_layers": 7,
   "num_hidden_layers": 24,
-  "pad_token_id": 74,
+  "pad_token_id": 93,
   "transformers_version": "4.5.0.dev0",
-  "vocab_size": 75
+  "vocab_size": 94
 }

pytorch_model.bin

@@ -1,3 +1,3 @@
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-size 1262241303
+oid sha256:be0fd803682bd53a5e10531a8c98d1d154a4303d6d03d28a07c09b221c0e324e
+size 1262319255

vocab.json

@@ -1 +1 @@
-{"प": 0, "ग": 1, "ौ": 2, "घ": 3, "झ": 4, "ख़": 5, "ष": 6, "छ": 7, "च": 8, "ढ़": 9, "ॅ": 10, "ऱ": 11, "ा": 12, "ख": 13, "ँ": 14, "ड": 15, "ह": 16, "श": 17, "ि": 18, "उ": 19, "ो": 20, "ऊ": 21, "ॉ": 22, "ग़": 23, "ऋ": 24, "्": 25, "ः": 26, "ं": 27, "द": 28, "ळ": 29, "ठ": 30, "ृ": 31, "ल": 32, "ज़": 33, "क": 34, "त": 35, "ध": 36, "ई": 37, "र": 38, "म": 39, "ज": 41, "आ": 42, "भ": 43, "।": 44, "इ": 45, "फ़": 46, "ञ": 47, "य": 48, "ऐ": 49, "ऑ": 50, "ट": 51, "ङ": 52, "थ": 53, "ी": 54, "ु": 55, "े": 56, "ै": 57, "न": 58, "स": 59, "ू": 60, "ब": 61, "ओ": 62, "व": 63, "ड़": 64, "अ": 65, "औ": 66, "़": 67, "क़": 68, "फ": 69, "ढ": 70, "ए": 71, "ण": 72, "/": 40, "[UNK]": 73, "[PAD]": 74}
+{"प": 0, "ण": 1, "ड": 2, "फ": 3, "फ़": 4, "ं": 5, "?": 6, "ष": 7, "ँ": 8, "ृ": 9, "थ": 10, "ऊ": 11, "u": 12, "ग": 13, "r": 14, "द": 15, "र": 16, "ी": 17, "झ": 18, "ः": 19, "ब": 20, "ऐ": 21, ".": 22, "ख": 23, "घ": 24, "स": 25, "ौ": 26, "ई": 27, "F": 28, "उ": 29, "छ": 30, "ऋ": 31, "ि": 32, "े": 33, "!": 34, "ू": 35, "न": 37, "आ": 38, "ै": 39, "ऑ": 40, "भ": 41, ",": 42, "्": 43, "य": 44, "ञ": 45, "ढ": 46, "इ": 47, "।": 48, "क": 49, "ऱ": 50, "ङ": 51, "ॅ": 52, "ओ": 53, "ज़": 54, "ॉ": 55, "e": 56, "p": 57, "a": 58, "l": 59, "M": 60, "ध": 61, "ा": 62, "व": 63, "ट": 64, ":": 65, "\"": 66, "'": 67, "ढ़": 68, "ड़": 69, "ो": 70, " ": 71, "ल": 72, "अ": 73, "क़": 74, "त": 75, "ु": 76, "औ": 77, "m": 78, "श": 79, "च": 80, "़": 81, "ए": 82, "ह": 83, "W": 84, "ग़": 85, "ठ": 86, "ज": 87, "-": 88, "i": 89, "ख़": 90, "म": 91, "/": 36, "[UNK]": 92, "[PAD]": 93}