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import gradio as gr
import requests
import os
# List of supported languages
LANGUAGES = ["malayalam", "tamil", "telugu", "hindi", "kannada"]
# Function to extract language name
def get_lang_name(lang_string):
return lang_string.split("(")[0].strip().lower()
def transcribe_api(audio_file, language):
# Get the base URL from environment variable
base_url = os.getenv("DWANI_AI_API_BASE_URL")
if not base_url:
return {"error": "DWANI_AI_API_BASE_URL environment variable is not set"}
# Define the endpoint path
endpoint = "v1/transcribe/?language"
# Construct the full API URL
url = f"{base_url.rstrip('/')}/{endpoint}={get_lang_name(language)}"
headers = {
"accept": "application/json",
}
try:
# Open the file in binary mode
with open(audio_file, 'rb') as f:
files = {
"file": (os.path.basename(audio_file), f, "audio/x-wav")
}
response = requests.post(url, headers=headers, files=files)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
return {"error": str(e)}
# Create Gradio interface
with gr.Blocks(title="Speech to Text API Interface") as demo:
gr.Markdown("# Speech to Text API Interface")
with gr.Row():
with gr.Column():
# Input components
audio_input = gr.Audio(
label="Audio File",
type="filepath",
sources=["upload"]
)
language_input = gr.Dropdown(
label="Language",
choices=LANGUAGES,
value="kannada"
)
submit_btn = gr.Button("Transcribe")
with gr.Column():
# Output component
output = gr.JSON(label="Transcription Response")
# Connect the button click to the API function
submit_btn.click(
fn=transcribe_api,
inputs=[audio_input, language_input],
outputs=output
)
# Launch the interface
demo.launch() |