| import gradio as gr | |
| from transformers import pipeline | |
| import numpy as np | |
| # Load the pipeline for speech recognition and translation | |
| pipe = pipeline( | |
| "automatic-speech-recognition", | |
| model="Baghdad99/saad-speech-recognition-hausa-audio-to-text", | |
| tokenizer="Baghdad99/saad-speech-recognition-hausa-audio-to-text" | |
| ) | |
| translator = pipeline("text2text-generation", model="Baghdad99/saad-hausa-text-to-english-text") | |
| tts = pipeline("text-to-speech", model="Baghdad99/english_voice_tts") | |
| def translate_speech(audio): | |
| # Separate the sample rate and the audio data | |
| sample_rate, audio_data = audio | |
| # Use the speech recognition pipeline to transcribe the audio | |
| output = pipe(audio_data) | |
| print(f"Output: {output}") # Print the output to see what it contains | |
| # Check if the output contains 'transcription' | |
| if 'transcription' in output: | |
| transcription = output["transcription"] | |
| else: | |
| print("The output does not contain 'transcription'") | |
| return | |
| # Rest of your code... | |
| # # Define the function to translate speech | |
| # def translate_speech(audio): | |
| # # Separate the sample rate and the audio data | |
| # sample_rate, audio_data = audio | |
| # # Use the speech recognition pipeline to transcribe the audio | |
| # transcription = pipe(audio_data)["transcription"] | |
| # # Use the translation pipeline to translate the transcription | |
| # translated_text = translator(transcription, return_tensors="pt", padding=True) | |
| # # Use the text-to-speech pipeline to synthesize the translated text | |
| # synthesised_speech = tts(translated_text, return_tensors='pt') | |
| # # Define the max_range variable | |
| # max_range = 32767 # You can adjust this value based on your requirements | |
| # synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16) | |
| # return 16000, synthesised_speech | |
| # Define the Gradio interface | |
| iface = gr.Interface( | |
| fn=translate_speech, | |
| inputs=gr.inputs.Audio(source="microphone", type="numpy"), | |
| outputs=gr.outputs.Audio(type="numpy"), | |
| title="Hausa to English Translation", | |
| description="Realtime demo for Hausa to English translation using speech recognition and text-to-speech synthesis." | |
| ) | |
| iface.launch() | |