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--- |
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license: apache-2.0 |
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language: |
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- bn |
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base_model: |
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- openai/whisper-small |
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pipeline_tag: automatic-speech-recognition |
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--- |
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BengaliRegionalASR trained on bengali regional dialact dataset. [sha1779/Bengali_Regional_dataset](https://huggingface.co/datasets/sha1779/Bengali_Regional_dataset) |
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This model is trained on this barishal regional data only. The dataset is taken from [ভাষা-বিচিত্রা: ASR for Regional Dialects](https://www.kaggle.com/competitions/ben10) competition. |
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# Try the model |
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```bash |
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!pip install librosa torch torchaudio transformers |
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``` |
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```py |
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import os |
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import librosa |
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import torch, torchaudio |
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import numpy as np |
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from transformers import WhisperTokenizer ,WhisperProcessor, WhisperFeatureExtractor, WhisperForConditionalGeneration |
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model_path_ = "sha1779/BengaliRegionalASR" |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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feature_extractor = WhisperFeatureExtractor.from_pretrained(model_path_) |
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tokenizer = WhisperTokenizer.from_pretrained(model_path_) |
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processor = WhisperProcessor.from_pretrained(model_path_) |
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model = WhisperForConditionalGeneration.from_pretrained(model_path_).to(device) |
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model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="bengali", task="transcribe") |
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mp3_path = "https://huggingface.co/sha1779/BengaliRegionalASR/resolve/main/Mp3/valid_barishal%20(1).wav" |
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speech_array, sampling_rate = librosa.load(mp3_path, sr=16000) |
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speech_array = librosa.resample(np.asarray(speech_array), orig_sr=sampling_rate, target_sr=16000) |
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input_features = feature_extractor(speech_array, sampling_rate=16000, return_tensors="pt").input_features |
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predicted_ids = model.generate(inputs=input_features.to(device))[0] |
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transcription = processor.decode(predicted_ids, skip_special_tokens=True) |
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print(transcription) |
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``` |
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## For larger audio , more than 30s |
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```py |
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import os |
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import librosa |
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import torch, torchaudio |
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import numpy as np |
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from transformers import WhisperTokenizer ,WhisperProcessor, WhisperFeatureExtractor, WhisperForConditionalGeneration |
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model_path_ = "sha1779/BengaliRegionalASR" |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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feature_extractor = WhisperFeatureExtractor.from_pretrained(model_path_) |
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tokenizer = WhisperTokenizer.from_pretrained(model_path_) |
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processor = WhisperProcessor.from_pretrained(model_path_) |
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model = WhisperForConditionalGeneration.from_pretrained(model_path_).to(device) |
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model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="bengali", task="transcribe") |
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mp3_path = "https://huggingface.co/sha1779/BengaliRegionalASR/resolve/main/Mp3/valid_barishal%20(1).wav" |
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speech_array, sampling_rate = librosa.load(mp3_path, sr=16000) |
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# Split audio into 30-second chunks with 5-second overlap |
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chunk_duration = 30 # seconds |
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overlap = 5 # seconds |
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chunk_size = int(chunk_duration * sampling_rate) |
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overlap_size = int(overlap * sampling_rate) |
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chunks = [] |
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for start in range(0, len(speech_array), chunk_size - overlap_size): |
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end = start + chunk_size |
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chunk = speech_array[start:end] |
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chunks.append(chunk) |
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# Process each chunk |
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transcriptions = [] |
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for i, chunk in enumerate(chunks): |
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# Resample and extract features |
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chunk = librosa.resample(np.asarray(chunk), orig_sr=sampling_rate, target_sr=16000) |
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input_features = feature_extractor(chunk, sampling_rate=16000, return_tensors="pt").input_features |
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# Generate transcription |
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predicted_ids = model.generate(inputs=input_features.to(device))[0] |
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transcription = processor.decode(predicted_ids, skip_special_tokens=True) |
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print(transcription,end=" ") |
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``` |
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# Evaluation |
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Word Error Rate 0.65 % |
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