Commit
•
0a1bc74
1
Parent(s):
d7aa717
updated model weights
Browse files- .ipynb_checkpoints/README-checkpoint.md +115 -0
- .ipynb_checkpoints/vocab-checkpoint.json +1 -0
- config.json +1 -1
- optimizer.pt +1 -1
- pytorch_model.bin +2 -2
- scheduler.pt +1 -1
- trainer_state.json +157 -877
- training_args.bin +1 -1
.ipynb_checkpoints/README-checkpoint.md
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+
---
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language: mr
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datasets:
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- openslr
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- interspeech_2021_asr
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metrics:
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- wer
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tags:
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- audio
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- automatic-speech-recognition
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- speech
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- xlsr-fine-tuning-week
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- hindi
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- marathi
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license: apache-2.0
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model-index:
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- name: XLSR Wav2Vec2 Large 53 Hindi-Marathi by Tanmay Laud
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results:
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- task:
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name: Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: OpenSLR hi, OpenSLR mr
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type: openslr, interspeech_2021_asr
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metrics:
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- name: Test WER
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type: wer
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value: 24.92
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---
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# Wav2Vec2-Large-XLSR-53-Hindi-Marathi
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Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hindi and Marathi using the OpenSLR SLR64 datasets. When using this model, make sure that your speech input is sampled at 16kHz.
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## Usage
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The model can be used directly (without a language model) as follows, assuming you have a dataset with Marathi text and audio_path fields:
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```
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import torch
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import torchaudio
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import librosa
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from datasets import load_dataset
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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# test_data = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section.
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processor = Wav2Vec2Processor.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi")
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model = Wav2Vec2ForCTC.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi")
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+
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# Preprocessing the datasets.
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# We need to read the audio files as arrays
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def speech_file_to_array_fn(batch):
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speech_array, sampling_rate = torchaudio.load(batch["audio_path"])
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batch["speech"] = librosa.resample(speech_array[0].numpy(), sampling_rate, 16_000) # sampling_rate can vary
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return batch
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test_data= test_data.map(speech_file_to_array_fn)
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inputs = processor(test_data["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
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with torch.no_grad():
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logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
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predicted_ids = torch.argmax(logits, dim=-1)
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print("Prediction:", processor.batch_decode(predicted_ids))
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print("Reference:", test_data["text"][:2])
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Evaluation
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The model can be evaluated as follows on 10% of the Marathi data on OpenSLR.
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```
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```
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import torchaudio
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from datasets import load_metric
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from transformers import Wav2Vec2Processor,Wav2Vec2ForCTC
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import torch
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import librosa
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import numpy as np
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import re
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wer = load_metric("wer")
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processor = Wav2Vec2Processor.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi")
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model = Wav2Vec2ForCTC.from_pretrained("tanmaylaud/wav2vec2-large-xlsr-hindi-marathi")
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model.to("cuda")
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\।]'
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# Preprocessing the datasets.
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# We need to read the audio files as arrays
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def speech_file_to_array_fn(batch):
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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"])
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speech_array, sampling_rate = torchaudio.load(batch["path"])
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batch["speech"] = speech_array[0].numpy()
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batch["sampling_rate"] = sampling_rate
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batch["target_text"] = batch["sentence"]
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batch["speech"] = librosa.resample(np.asarray(batch["speech"]), sampling_rate, 16_000)
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batch["sampling_rate"] = 16_000
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return batch
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test= test.map(speech_file_to_array_fn)
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# Preprocessing the datasets.
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# We need to read the audio files as arrays
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def evaluate(batch):
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inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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with torch.no_grad():
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logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
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pred_ids = torch.argmax(logits, dim=-1)
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batch["pred_strings"] = processor.batch_decode(pred_ids, group_tokens=False)
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# we do not want to group tokens when computing the metrics
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return batch
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result = test.map(evaluate, batched=True, batch_size=32)
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["text"])))
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```
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Link to eval notebook : https://colab.research.google.com/drive/1nZRTgKfxCD9cvy90wikTHkg2il3zgcqW#scrollTo=cXWFbhb0d7DT
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.ipynb_checkpoints/vocab-checkpoint.json
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{"ँ": 1, "ं": 2, "ः": 3, "अ": 4, "आ": 5, "इ": 6, "ई": 7, "उ": 8, "ऊ": 9, "ऋ": 10, "ऍ": 11, "ए": 12, "ऐ": 13, "ऑ": 14, "ओ": 15, "औ": 16, "क": 17, "ख": 18, "ग": 19, "घ": 20, "च": 21, "छ": 22, "ज": 23, "झ": 24, "ञ": 25, "ट": 26, "ठ": 27, "ड": 28, "ढ": 29, "ण": 30, "त": 31, "थ": 32, "द": 33, "ध": 34, "न": 35, "प": 36, "फ": 37, "ब": 38, "भ": 39, "म": 40, "य": 41, "र": 42, "ल": 43, "ळ": 44, "व": 45, "श": 46, "ष": 47, "स": 48, "ह": 49, "़": 50, "ा": 51, "ि": 52, "ी": 53, "ु": 54, "ू": 55, "ृ": 56, "ॅ": 57, "े": 58, "ै": 59, "ॉ": 60, "ो": 61, "ौ": 62, "्": 63, "क़": 64, "ख़": 65, "ग़": 66, "ज़": 67, "ड़": 68, "ढ़": 69, "फ़": 70, "ॠ": 71, "|": 0, "[UNK]": 72, "[PAD]": 73}
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config.json
CHANGED
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{
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-
"_name_or_path": "
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"activation_dropout": 0.1,
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"apply_spec_augment": true,
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"architectures": [
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{
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"_name_or_path": "/workspace/output_models/hi-mr/wav2vec2-large-xlsr-hindi-marathi-3/pretrained/checkpoint-6200",
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"activation_dropout": 0.1,
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"apply_spec_augment": true,
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"architectures": [
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optimizer.pt
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trainer_state.json
CHANGED
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{
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-
"best_metric": 0.
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-
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-
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| 21 |
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|
| 289 |
}
|
| 290 |
],
|
| 291 |
+
"max_steps": 6300,
|
| 292 |
+
"num_train_epochs": 10,
|
| 293 |
+
"total_flos": 2.1761689418766148e+19,
|
| 294 |
"trial_name": null,
|
| 295 |
"trial_params": null
|
| 296 |
}
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 2351
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d4e43089cce3509942d599d030fda34b6b77b40e5839002693f8638c39f46025
|
| 3 |
size 2351
|