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--- |
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license: apache-2.0 |
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language: |
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- wo |
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metrics: |
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- wer |
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pipeline_tag: automatic-speech-recognition |
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model-index: |
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- name: wav2vec2-large-xlsr-53-wolof |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: WOLOF Speech Recognition |
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metrics: |
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- type: wer |
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value: 7.88 |
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name: WER |
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config: load_metric("wer") |
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verified: true |
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--- |
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## Evaluation on WOLOF Test |
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[](https://github.com/kingabzpro/WOLOF-ASR-Wav2Vec2) |
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```python |
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import pandas as pd |
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from datasets import load_dataset, load_metric,Dataset |
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from tqdm import tqdm |
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import torch |
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import soundfile as sf |
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import torchaudio |
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from transformers import Wav2Vec2ForCTC |
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from transformers import Wav2Vec2Processor |
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from transformers import Wav2Vec2FeatureExtractor |
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from transformers import Wav2Vec2CTCTokenizer |
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model_name = "kingabzpro/wav2vec2-large-xlsr-53-wolof" |
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device = "cuda" |
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model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) |
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processor = Wav2Vec2Processor.from_pretrained(model_name) |
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val =pd.read_csv("../input/automatic-speech-recognition-in-wolof/Test.csv") |
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val["path"] = "../input/automatic-speech-recognition-in-wolof/Noise Removed/tmp/WOLOF_ASR_dataset/noise_remove/"+val["ID"]+".wav" |
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val.rename(columns = {'transcription':'sentence'}, inplace = True) |
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common_voice_val = Dataset.from_pandas(val) |
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def speech_file_to_array_fn_test(batch): |
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speech_array, sampling_rate = sf.read(batch["path"])#(.wav) 16000 sample rate |
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batch["speech"] = speech_array |
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batch["sampling_rate"] = sampling_rate |
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return batch |
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def prepare_dataset_test(batch): |
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# check that all files have the correct sampling rate |
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assert ( |
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len(set(batch["sampling_rate"])) == 1 |
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), f"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}." |
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batch["input_values"] = processor(batch["speech"], padding=True,sampling_rate=batch["sampling_rate"][0]).input_values |
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return batch |
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common_voice_val = common_voice_val.remove_columns([ "ID","age", "down_votes", "gender", "up_votes"]) # Remove columns |
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common_voice_val = common_voice_val.map(speech_file_to_array_fn_test, remove_columns=common_voice_val.column_names)# Applying speech_file_to_array function |
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common_voice_val = common_voice_val.map(prepare_dataset_test, remove_columns=common_voice_val.column_names, batch_size=8, num_proc=4, batched=True)# Applying prepare_dataset_test function |
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final_pred = [] |
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for i in tqdm(range(common_voice_val.shape[0])):# Testing model on Wolof Dataset |
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input_dict = processor(common_voice_val[i]["input_values"], return_tensors="pt", padding=True) |
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logits = model(input_dict.input_values.to("cuda")).logits |
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pred_ids = torch.argmax(logits, dim=-1)[0] |
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prediction = processor.decode(pred_ids) |
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final_pred.append(prediction) |
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``` |
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You can check my result on [Zindi](https://zindi.africa/competitions/ai4d-baamtu-datamation-automatic-speech-recognition-in-wolof/leaderboard), I got 8th rank in AI4D Baamtu Datamation - Automatic Speech Recognition in WOLOF |
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**Result**: 7.88 % |
