Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| import requests | |
| import soundfile as sf | |
| import numpy as np | |
| import tempfile | |
| from pydub import AudioSegment | |
| import io # Add this line | |
| # Define the Hugging Face Inference API URLs and headers | |
| ASR_API_URL = "https://api-inference.huggingface.co/models/Baghdad99/saad-speech-recognition-hausa-audio-to-text" | |
| TTS_API_URL = "https://api-inference.huggingface.co/models/Baghdad99/english_voice_tts" | |
| TRANSLATION_API_URL = "https://api-inference.huggingface.co/models/Baghdad99/saad-hausa-text-to-english-text" | |
| headers = {"Authorization": "Bearer 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"} | |
| # Define the function to query the Hugging Face Inference API | |
| def query(api_url, payload): | |
| response = requests.post(api_url, headers=headers, json=payload) | |
| return response.json() | |
| # Define the function to translate speech | |
| def translate_speech(audio): | |
| print(f"Type of audio: {type(audio)}, Value of audio: {audio}") # Debug line | |
| # audio is a tuple (np.ndarray, int), we need to save it as a file | |
| sample_rate, audio_data = audio | |
| if isinstance(audio_data, np.ndarray) and len(audio_data.shape) == 1: # if audio_data is 1D, reshape it to 2D | |
| audio_data = np.reshape(audio_data, (-1, 1)) | |
| with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f: | |
| sf.write(f, audio_data, sample_rate) | |
| audio_file = f.name | |
| # Use the ASR pipeline to transcribe the audio | |
| with open(audio_file, "rb") as f: | |
| data = f.read() | |
| response = requests.post(ASR_API_URL, headers=headers, data=data) | |
| output = response.json() | |
| # Check if the output contains 'text' | |
| if 'text' in output: | |
| transcription = output["text"] | |
| else: | |
| print("The output does not contain 'text'") | |
| return | |
| # Use the translation pipeline to translate the transcription | |
| translated_text = query(TRANSLATION_API_URL, {"inputs": transcription}) | |
| # Use the TTS pipeline to synthesize the translated text | |
| response = requests.post(TTS_API_URL, headers=headers, json={"inputs": translated_text}) | |
| audio_bytes = response.content | |
| # Convert the audio bytes to an audio segment | |
| audio_segment = AudioSegment.from_mp3(io.BytesIO(audio_bytes)) | |
| # Convert the audio segment to a numpy array | |
| audio_data = np.array(audio_segment.get_array_of_samples()) | |
| if audio_segment.channels == 2: | |
| audio_data = audio_data.reshape((-1, 2)) | |
| return audio_data | |
| # 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() | |