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import traceback
import soundfile as sf
import torch
import numpy as np
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
import gradio as gr
import resampy
# Language code mapping
LANGUAGE_CODES = {
"Amharic": "amh",
"Swahili": "swh",
"Somali": "som",
"Afan Oromo": "orm",
"Tigrinya": "tir",
"Chichewa": "nya"
}
# --- Load ASR model ---
try:
model_id = "facebook/seamless-m4t-v2-large"
processor = AutoProcessor.from_pretrained(model_id)
asr_model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id).to("cpu")
print("[INFO] ASR model loaded successfully.")
except Exception as e:
print("[ERROR] Failed to load ASR model:", e)
traceback.print_exc()
asr_model = None
processor = None
# --- Helper: ASR ---
def transcribe_audio(audio_file, language):
if asr_model is None or processor is None:
return "ASR Model loading failed"
try:
# Get language code
lang_code = LANGUAGE_CODES.get(language)
if not lang_code:
return f"Unsupported language: {language}"
# Read and preprocess audio
audio, sr = sf.read(audio_file)
if audio.ndim > 1:
audio = audio.mean(axis=1)
audio = resampy.resample(audio, sr, 16000)
# Process with model
inputs = processor(audios=audio, sampling_rate=16000, return_tensors="pt")
with torch.no_grad():
generated_ids = asr_model.generate(**inputs, tgt_lang=lang_code)
# Decode the transcription
transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return transcription.strip()
except Exception as e:
print(f"[ERROR] ASR transcription failed for {language}:", e)
traceback.print_exc()
return f"ASR failed: {str(e)[:50]}..."
# --- Gradio UI ---
with gr.Blocks(title="🌍 Multilingual ASR") as demo:
gr.Markdown("# 🌍 Multilingual Speech Recognition")
gr.Markdown("Transcribe audio in Amharic, Swahili, Somali, Afan Oromo, Tigrinya, or Chichewa")
with gr.Row():
with gr.Column():
audio_input = gr.Audio(sources=["microphone", "upload"], type="filepath", label="Record or upload audio")
language_select = gr.Dropdown(
choices=list(LANGUAGE_CODES.keys()),
value="Swahili",
label="Select Language"
)
submit_btn = gr.Button("Transcribe", variant="primary")
with gr.Row():
with gr.Column():
transcription_output = gr.Textbox(label="Transcription")
submit_btn.click(
fn=transcribe_audio,
inputs=[audio_input, language_select],
outputs=transcription_output
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860) |