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# app.py
import gradio as gr
from transformers import pipeline
import soundfile as sf
import numpy as np
# --- EDIT THIS: map display names to your HF Hub model IDs ---
language_models = {
"Akan (Asanti 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",
# add more as needed
}
# Pre-load pipelines for each language
asr_pipelines = {
lang: pipeline(
task="automatic-speech-recognition",
model=model_id,
# device=0, # uncomment if you have GPU
chunk_length_s=30 # adjust if your audio can be longer
)
for lang, model_id in language_models.items()
}
def transcribe(audio_path: str, language: str) -> str:
"""
Load the audio file, convert to mono if needed,
and run it through the selected ASR pipeline.
"""
if audio_path is None:
return "⚠️ Please upload or record an audio clip."
# Read the file
speech, sr = sf.read(audio_path)
# Stereo → mono
if speech.ndim > 1:
speech = np.mean(speech, axis=1)
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 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()
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