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README.md ADDED
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+ ---
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+ language: id
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+ datasets:
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+ - common_voice
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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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+ license: apache-2.0
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+ model-index:
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+ - name: XLSR Wav2Vec2 Indonesian by Galuh
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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: Common Voice id
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+ type: common_voice
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+ args: id
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+ metrics:
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+ - name: Test WER
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+ type: wer
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+ value: 20.67
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+ ---
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+
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+ # Wav2Vec2-Large-XLSR-Indonesian
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+
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+ This is the model for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned
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+ [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
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+ model on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice).
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+ When using this model, make sure that your speech input is sampled at 16kHz.
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+
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+ ## Usage
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+ The model can be used directly (without a language model) as follows:
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+ ```python
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+ import torch
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+ import torchaudio
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+ from datasets import load_dataset
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+ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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+
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+ test_dataset = load_dataset("common_voice", "id", split="test[:2%]")
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+
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+ processor = Wav2Vec2Processor.from_pretrained("Galuh/wav2vec2-large-xlsr-indonesian")
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+ model = Wav2Vec2ForCTC.from_pretrained("Galuh/wav2vec2-large-xlsr-indonesian")
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+
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+ resampler = torchaudio.transforms.Resample(48_000, 16_000)
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+
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+ # Preprocessing the datasets.
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+ # We need to read the aduio 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["path"])
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+ batch["speech"] = resampler(speech_array).squeeze().numpy()
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+ return batch
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+
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+ test_dataset = test_dataset.map(speech_file_to_array_fn)
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+ inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
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+
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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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+
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+ predicted_ids = torch.argmax(logits, dim=-1)
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+
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+ print("Prediction:", processor.batch_decode(predicted_ids))
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+ print("Reference:", test_dataset["sentence"][:2])
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+ ```
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+
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+
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+ ## Evaluation
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+
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+ The model can be evaluated as follows on the Indonesian test data of Common Voice.
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+
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+ ```python
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+ import torch
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+ import torchaudio
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+ from datasets import load_dataset, load_metric
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+ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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+ import re
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+
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+ test_dataset = load_dataset("common_voice", "id", split="test")
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+ wer = load_metric("wer")
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+
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+ processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-large-xlsr-indonesian-baselin")
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+ model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-large-xlsr-indonesian-baseline")
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+ model.to("cuda")
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+
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+ chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]'
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+
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+ resampler = torchaudio.transforms.Resample(48_000, 16_000)
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+
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+ # Preprocessing the datasets.
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+ # We need to read the aduio 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"]).lower()
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+ speech_array, sampling_rate = torchaudio.load(batch["path"])
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+ batch["speech"] = resampler(speech_array).squeeze().numpy()
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+ return batch
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+
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+ test_dataset = test_dataset.map(speech_file_to_array_fn)
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+
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+ # Preprocessing the datasets.
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+ # We need to read the aduio 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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+
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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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+
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+ pred_ids = torch.argmax(logits, dim=-1)
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+ batch["pred_strings"] = processor.batch_decode(pred_ids)
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+ return batch
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+
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+ result = test_dataset.map(evaluate, batched=True, batch_size=8)
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+
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+ print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
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+ ```
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+
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+ **Test Result**: 20.67 %
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+
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+ ## Training
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+
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+ The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
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+
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+ The script used for training can be found [here](https://github.com/galuhsahid/wav2vec2-indonesian)
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+ (will be available soon)
config.json ADDED
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+ {
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+ "_name_or_path": "facebook/wav2vec2-large-xlsr-53",
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+ "activation_dropout": 0.055,
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+ "apply_spec_augment": true,
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+ "architectures": [
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+ "Wav2Vec2ForCTC"
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+ ],
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+ "attention_dropout": 0.094,
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+ "bos_token_id": 1,
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+ "conv_bias": true,
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+ "conv_dim": [
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+ 512,
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+ ],
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+ 2
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+ ],
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+ "conv_stride": [
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+ 5,
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+ 2
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+ ],
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+ "ctc_loss_reduction": "mean",
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+ "ctc_zero_infinity": true,
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+ "do_stable_layer_norm": true,
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+ "eos_token_id": 2,
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+ "feat_extract_activation": "gelu",
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+ "feat_extract_dropout": 0.0,
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+ "feat_extract_norm": "layer",
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+ "feat_proj_dropout": 0.04,
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+ "final_dropout": 0.0,
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+ "gradient_checkpointing": true,
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+ "hidden_act": "gelu",
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+ "hidden_dropout": 0.047,
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+ "hidden_size": 1024,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 4096,
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+ "layer_norm_eps": 1e-05,
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+ "layerdrop": 0.041,
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+ "mask_channel_length": 10,
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+ "mask_channel_min_space": 1,
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+ "mask_channel_other": 0.0,
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+ "mask_channel_prob": 0.0,
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+ "mask_channel_selection": "static",
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+ "mask_feature_length": 10,
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+ "mask_feature_prob": 0.0,
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+ "mask_time_length": 10,
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+ "mask_time_min_space": 1,
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+ "mask_time_other": 0.0,
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+ "mask_time_prob": 0.4,
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+ "mask_time_selection": "static",
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+ "model_type": "wav2vec2",
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+ "num_attention_heads": 16,
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+ "num_conv_pos_embedding_groups": 16,
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+ "num_conv_pos_embeddings": 128,
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+ "num_feat_extract_layers": 7,
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+ "pad_token_id": 30,
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+ "transformers_version": "4.5.0.dev0",
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+ "vocab_size": 31
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+ }
preprocessor_config.json ADDED
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+ {
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+ "padding_side": "right",
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+ "padding_value": 0.0,
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+ "return_attention_mask": true,
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+ "sampling_rate": 16000
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+ }
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