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- .idea/.gitignore +3 -0
- .idea/asr-test.iml +10 -0
- .idea/inspectionProfiles/Project_Default.xml +136 -0
- .idea/inspectionProfiles/profiles_settings.xml +6 -0
- .idea/misc.xml +7 -0
- .idea/modules.xml +8 -0
- .idea/vcs.xml +6 -0
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.idea/.gitignore
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# Default ignored files
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/shelf/
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/workspace.xml
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.idea/asr-test.iml
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<?xml version="1.0" encoding="UTF-8"?>
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.idea/inspectionProfiles/Project_Default.xml
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.idea/inspectionProfiles/profiles_settings.xml
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<component name="InspectionProjectProfileManager">
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<settings>
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<option name="USE_PROJECT_PROFILE" value="false" />
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<version value="1.0" />
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="Black">
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<option name="sdkName" value="Python 3.8 (asr-test)" />
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</component>
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (asr-test)" project-jdk-type="Python SDK" />
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</project>
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.idea/modules.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectModuleManager">
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<modules>
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<module fileurl="file://$PROJECT_DIR$/.idea/asr-test.iml" filepath="$PROJECT_DIR$/.idea/asr-test.iml" />
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</modules>
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</project>
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.idea/vcs.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="VcsDirectoryMappings">
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<mapping directory="" vcs="Git" />
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</component>
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</project>
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app.py
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import gradio as gr
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# Create a list of example audio files
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examples = [
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["https://huggingface.co/MedAiHealth/BanglaASR-MedAI/resolve/main/wav/sample_1.wav"],
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["https://huggingface.co/MedAiHealth/BanglaASR-MedAI/resolve/main/wav/sample_2.wav"],
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["https://huggingface.co/MedAiHealth/BanglaASR-MedAI/resolve/main/wav/sample_3.wav"]
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]
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# Create the Gradio interface
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interface = gr.Interface(
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fn=
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inputs=gr.Audio(sources="microphone", type="filepath"),
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outputs=gr.Textbox(),
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examples=examples
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)
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# Launch the interface
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interface.launch()
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import os
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import gradio as gr
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import librosa
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import torch
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import torchaudio
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import numpy as np
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from transformers import WhisperTokenizer
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from transformers import WhisperProcessor
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from transformers import WhisperFeatureExtractor
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from transformers import WhisperForConditionalGeneration
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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model_path = os.environ.get("HF_REPO_ID")
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access_token = os.environ.get("HF_TOKEN")
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feature_extractor = WhisperFeatureExtractor.from_pretrained(model_path, token=access_token)
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tokenizer = WhisperTokenizer.from_pretrained(model_path, token=access_token)
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processor = WhisperProcessor.from_pretrained(model_path, token=access_token)
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model = WhisperForConditionalGeneration.from_pretrained(model_path, token=access_token).to(device)
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def transcribe_audio(file_path):
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speech_array, sampling_rate = torchaudio.load(file_path, format="wav")
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speech_array = speech_array[0].numpy()
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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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# batch = processor.feature_extractor.pad(input_features, return_tensors="pt")
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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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return transcription
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# Create a list of example audio files
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examples = [f"test_sample/{x}" for x in os.listdir("test_sample")]
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# Create the Gradio interface
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interface = gr.Interface(
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fn=transcribe_audio,
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inputs=gr.Audio(sources="microphone", type="filepath"),
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outputs=gr.Textbox(),
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examples=examples
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)
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# Launch the interface
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interface.launch()
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test_sample/2022_05_23-05_20_09.544938_file_file.wav
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test_sample/2022_06_28-12_01_43.552306_file_file.wav
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