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| # app.py | |
| import gradio as gr | |
| from transformers import pipeline | |
| import numpy as np | |
| import librosa # pip install librosa | |
| # --- EDIT THIS: map display names to your HF Hub model IDs --- | |
| language_models = { | |
| "Akan (Asante Twi)": "FarmerlineML/w2v-bert-2.0_twi_alpha_v1", | |
| "Ewe": "FarmerlineML/w2v-bert-2.0_ewe_2", | |
| "Kiswahili": "FarmerlineML/w2v-bert-2.0_swahili_alpha", | |
| # "Luganda": "FarmerlineML/w2v-bert-2.0_luganda", | |
| "Brazilian Portuguese": "FarmerlineML/w2v-bert-2.0_brazilian_portugese_alpha", | |
| "Fante": "misterkissi/w2v2-lg-xls-r-300m-fante", | |
| "Bemba": "DarliAI/kissi-w2v2-lg-xls-r-300m-bemba", | |
| "Bambara": "DarliAI/kissi-w2v2-lg-xls-r-300m-bambara", | |
| "Dagaare": "DarliAI/kissi-w2v2-lg-xls-r-300m-dagaare", | |
| "Kinyarwanda": "DarliAI/kissi-w2v2-lg-xls-r-300m-kinyarwanda", | |
| "Fula": "DarliAI/kissi-wav2vec2-fula-fleurs-full", | |
| "Oromo": "DarliAI/kissi-w2v-bert-2.0-oromo", | |
| "Runynakore": "misterkissi/w2v2-lg-xls-r-300m-runyankore", | |
| "Ga": "misterkissi/w2v2-lg-xls-r-300m-ga", | |
| "Vai": "misterkissi/whisper-small-vai", | |
| "Kasem": "misterkissi/w2v2-lg-xls-r-300m-kasem", | |
| "Lingala": "misterkissi/w2v2-lg-xls-r-300m-lingala", | |
| "Fongbe": "misterkissi/whisper-small-fongbe", | |
| "Amharic": "misterkissi/w2v2-lg-xls-r-1b-amharic", | |
| "Xhosa": "misterkissi/w2v2-lg-xls-r-300m-xhosa", | |
| "Tsonga": "misterkissi/w2v2-lg-xls-r-300m-tsonga", | |
| # "WOLOF": "misterkissi/w2v2-lg-xls-r-1b-wolof", | |
| # "HAITIAN CREOLE": "misterkissi/whisper-small-haitian-creole", | |
| # "KABYLE": "misterkissi/w2v2-lg-xls-r-1b-kabyle", | |
| "Yoruba": "FarmerlineML/w2v-bert-2.0_yoruba_v1", | |
| "Luganda": "FarmerlineML/luganda_fkd", | |
| "Luo": "FarmerlineML/w2v-bert-2.0_luo_v2", | |
| "Somali": "FarmerlineML/w2v-bert-2.0_somali_alpha", | |
| "Pidgin": "FarmerlineML/pidgin_nigerian", | |
| "Kikuyu": "FarmerlineML/w2v-bert-2.0_kikuyu", | |
| "Igbo": "FarmerlineML/w2v-bert-2.0_igbo_v1", | |
| #"Krio": "FarmerlineML/w2v-bert-2.0_krio_v3" | |
| # add more as needed | |
| } | |
| # Pre-load pipelines for each language on CPU (device=-1) | |
| asr_pipelines = { | |
| lang: pipeline( | |
| task="automatic-speech-recognition", | |
| model=model_id, | |
| device=-1, # force CPU usage | |
| chunk_length_s=30 | |
| ) | |
| for lang, model_id in language_models.items() | |
| } | |
| def transcribe(audio_path: str, language: str) -> str: | |
| """ | |
| Load the audio via librosa (supports mp3, wav, flac, m4a, ogg, etc.), | |
| convert to mono, then run it through the chosen ASR pipeline. | |
| """ | |
| if not audio_path: | |
| return "⚠️ Please upload or record an audio clip." | |
| # librosa.load returns a 1D np.ndarray (mono) and the sample rate | |
| speech, sr = librosa.load(audio_path, sr=None, mono=True) | |
| # Call the Hugging Face ASR pipeline | |
| result = asr_pipelines[language]({ | |
| "sampling_rate": sr, | |
| "raw": speech | |
| }) | |
| return result.get("text", "") | |
| with gr.Blocks(title="🌐 Multilingual ASR Demo") as demo: | |
| gr.Markdown( | |
| """ | |
| ## 🎙️ Multilingual Speech-to-Text | |
| Upload an audio file (MP3, WAV, FLAC, M4A, OGG,…) or record via your microphone. | |
| Then choose the language/model and hit **Transcribe**. | |
| """ | |
| ) | |
| with gr.Row(): | |
| lang = gr.Dropdown( | |
| choices=list(language_models.keys()), | |
| value=list(language_models.keys())[0], | |
| label="Select Language / Model" | |
| ) | |
| with gr.Row(): | |
| audio = gr.Audio( | |
| sources=["upload", "microphone"], | |
| type="filepath", | |
| label="Upload or Record Audio" | |
| ) | |
| btn = gr.Button("Transcribe") | |
| output = gr.Textbox(label="Transcription") | |
| btn.click(fn=transcribe, inputs=[audio, lang], outputs=output) | |
| if __name__ == "__main__": | |
| demo.launch() |