| import gradio, requests, os | |
| API_URL = os.environ["API_URL"] | |
| HEADERS = {"Authorization": os.environ["TOKEN"]} | |
| def predict(audio, lang="pa"): | |
| try: | |
| response = requests.post( | |
| API_URL, | |
| headers=HEADERS, | |
| files=[("file", ("audio.wav", open(audio, "rb"), "audio/wave"))], | |
| data={ | |
| "lang": lang, | |
| "include_translation": True, | |
| }, | |
| ) | |
| data = response.json() | |
| return data["transcription"], data["translation"] | |
| except Exception: | |
| return "Model was sleeping and a request has been sent to wake it up. Please try again after 3 mins.", "" | |
| gradio.Interface( | |
| fn=predict, | |
| inputs=[ | |
| gradio.Audio( | |
| type="filepath", | |
| format="wav", | |
| label="Speak in Panjabi or English", | |
| sources=["microphone"], | |
| max_length=60, | |
| ), | |
| gradio.Radio( | |
| choices={("English", "en"), ("Panjabi", "pa")}, | |
| value="pa", | |
| label="Select your speech language", | |
| ), | |
| ], | |
| outputs=[gradio.Label(label="Transcription"), gradio.Label(label="Translation")], | |
| title="Faster whisper model trained to recognise Panjabi-English speech (WER 12.9)", | |
| ).launch() | |