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from transformers import pipeline |
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from transformers import WhisperForConditionalGeneration, WhisperProcessor, WhisperFeatureExtractor |
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import gradio as gr |
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import librosa |
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MODEL_SPECS_ID = "dmatekenya/whisper-small_finetuned_sh_chich" |
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MODEL_SPECS_BASE_ID = "openai/whisper-small" |
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MODEL_SPECS_BASE_LAN_SW = "swahili" |
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MODEL_SPECS_BASE_LAN_SH = "shona" |
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FEATURE_EXTRACTOR = WhisperFeatureExtractor.from_pretrained(MODEL_SPECS_ID) |
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PROCESSOR_SH = WhisperProcessor.from_pretrained(MODEL_SPECS_BASE_ID, |
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language=MODEL_SPECS_BASE_LAN_SH, task="transcribe") |
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MODEL = WhisperForConditionalGeneration.from_pretrained(MODEL_SPECS_ID) |
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def transcribe(audio_file): |
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y, sr = librosa.load(audio_file, sr=16000) |
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input_features = PROCESSOR_SH(y, return_tensors="pt", sampling_rate=sr).input_features |
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generated_ids = MODEL.generate(inputs=input_features) |
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transcription = PROCESSOR_SH.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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return transcription |
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def transcribe_audio(mic=None, file=None): |
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if mic is not None: |
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audio = mic |
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elif file is not None: |
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audio = file |
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else: |
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return "You must either provide a mic recording or a file" |
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transcription = transcribe(audio_file=audio) |
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return transcription |
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title = "Transcribe Chichewa Audio-Whisper" |
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description = """ |
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<img src="https://i.ibb.co/5nQdGSs/logo.png"> |
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IN THIS DEMO, TEST THE FIRST AUTOMATED SPEECH RECOGNITION (ASR) MODEL FOR CHICHEWA BY TRANSCRIBING YOUR CHICHEWA VOICE NOTES. |
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FOR AUDIO FILES, PLEASE UPLOAD SHORT VOICE NOTES ONLY (NO LONGER THAN 30 SEC). |
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""" |
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article = "Read more about the [ChichewaSpeech2Text](https://dmatekenya.github.io/Chichewa-Speech2Text/README.html) project \ |
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and make sure to sign-up for our first [voice note donation event](https://forms.gle/fHLESutofVvb2YFM9) on July 22. \ |
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You stand a chance to win Airtel or TNM units if you choose to participate in the raffle after the event" |
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gr.Interface( |
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fn=transcribe_audio, |
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theme='grass', |
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title=title, |
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description=description, |
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article=article, |
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inputs=[ |
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gr.Audio(source="microphone", type="filepath", optional=True), |
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gr.Audio(source="upload", type="filepath", optional=True), |
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], |
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outputs="text", |
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).launch() |
