File size: 3,907 Bytes
cbf450b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | # -*- coding: utf-8 -*-
"""ASR_Deployment.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1MmePYOn1Ho2FhILi00u9UbvsujEoHhot
"""
import gradio as gr
from transformers import WhisperForConditionalGeneration, WhisperProcessor, GenerationConfig
import torch
import librosa
import os
# --- 1. CONFIGURATION ---
# Note: Ensure your token has "Read" access to the repository
MODEL_PATH = "MaryWambo/whisper-base-kikuyu4"
device = "cuda" if torch.cuda.is_available() else "cpu"
# --- 2. LOAD MODEL & PROCESSOR ---
print(f"Loading model to {device}...")
try:
processor = WhisperProcessor.from_pretrained(MODEL_PATH)
model = WhisperForConditionalGeneration.from_pretrained(MODEL_PATH).to(device)
# Define Generation Config to avoid "outdated" errors
# We set language and task here so they don't conflict in the generate() call
gen_config = GenerationConfig.from_pretrained(MODEL_PATH)
gen_config.language = "swahili" # Using full name or "sw" depending on how it was trained
gen_config.task = "transcribe"
gen_config.forced_decoder_ids = None
gen_config.suppress_tokens = []
model.generation_config = gen_config
except Exception as e:
print(f"Error loading model: {e}")
# --- 3. CUSTOM CSS ---
custom_css = """
body, .gradio-container { background-color: white !important; }
#title-text h1 { color: #8b0000 !important; font-weight: 900 !important; text-align: center; }
.upload-button svg, .mic-button svg, .clear-button svg, .record-button svg {
transform: scale(1.5) !important;
color: #8b0000 !important;
}
#predict-box textarea {
font-size: 1.6rem !important;
font-weight: 800 !important;
color: #000000 !important;
border: 3px solid #8b0000 !important;
}
#run-btn {
background: #8b0000 !important;
color: white !important;
font-weight: bold !important;
font-size: 1.4rem !important;
}
"""
# --- 4. LOGIC FUNCTIONS ---
def transcribe_kikuyu(audio):
if audio is None:
return "Please record or upload audio."
try:
# Load audio and resample to 16kHz (standard for Whisper)
speech_array, sampling_rate = librosa.load(audio, sr=16000)
# Process audio features
inputs = processor(speech_array, sampling_rate=sampling_rate, return_tensors="pt")
input_features = inputs.input_features.to(device)
with torch.no_grad():
# We no longer pass 'language' or 'task' here because
# they are already defined in model.generation_config
generated_ids = model.generate(
input_features=input_features,
num_beams=5,
max_new_tokens=255
)
# Decode the predicted IDs to text
prediction = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return prediction
except Exception as e:
return f"Error during transcription: {str(e)}"
# --- 5. BUILD GRADIO UI ---
with gr.Blocks(theme=gr.themes.Default(), css=custom_css) as demo:
gr.Markdown("# 🎙️ Kikuyu ASR ", elem_id="title-text")
with gr.Row():
with gr.Column(scale=1):
audio_input = gr.Audio(
sources=["microphone", "upload"],
type="filepath",
label="🎤 Record/Upload Kikuyu Speech"
)
submit_btn = gr.Button("🚀 RUN TRANSCRIPTION", elem_id="run-btn")
with gr.Column(scale=1):
text_out = gr.Textbox(
label="🤖 AI Prediction",
elem_id="predict-box",
lines=8
)
submit_btn.click(
fn=transcribe_kikuyu,
inputs=[audio_input],
outputs=[text_out]
)
# --- 6. LAUNCH ---
if __name__ == "__main__":
# share=True creates a public URL valid for 72 hours
demo.launch(share=True, debug=True)
|