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| import torch | |
| import gradio as gr | |
| from transformers import pipeline, AutoTokenizer, M2M100ForConditionalGeneration | |
| from tokenization_small100 import SMALL100Tokenizer | |
| import numpy as np | |
| from pydub import AudioSegment | |
| # Load the pipeline for speech recognition | |
| pipe = pipeline( | |
| "automatic-speech-recognition", | |
| model="DrishtiSharma/whisper-large-v2-hausa", | |
| tokenizer="DrishtiSharma/whisper-large-v2-hausa" | |
| ) | |
| # Load the new translation model and tokenizer | |
| model_name = 'alirezamsh/small100' | |
| model = M2M100ForConditionalGeneration.from_pretrained(model_name) | |
| tokenizer = SMALL100Tokenizer.from_pretrained(model_name) | |
| tts = pipeline("text-to-speech", model="Baghdad99/english_voice_tts") | |
| # Define the function to translate speech | |
| def translate_speech(audio_file): | |
| print(f"Type of audio: {type(audio_file)}, Value of audio: {audio_file}") # Debug line | |
| # Load the audio file with pydub | |
| audio = AudioSegment.from_mp3(audio_file) # Change this line | |
| # Convert the audio to mono and get the raw data | |
| audio = audio.set_channels(1) | |
| audio_data = np.array(audio.get_array_of_samples()) | |
| # Convert the numpy array to double | |
| audio_data = audio_data.astype(np.float64) | |
| # 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 'text' | |
| if 'text' in output: | |
| transcription = output["text"] | |
| else: | |
| print("The output does not contain 'text'") | |
| return | |
| # Use the new translation model to translate the transcription | |
| text = "translate Hausa to English: " + transcription | |
| tokenizer.tgt_lang = "en" | |
| encoded_text = tokenizer(text, return_tensors="pt") | |
| outputs = model.generate(**encoded_text) | |
| # Decode the tokens into text | |
| translated_text_str = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # Use the text-to-speech pipeline to synthesize the translated text | |
| synthesised_speech = tts(translated_text_str) | |
| # Check if the synthesised speech contains 'audio' | |
| if 'audio' in synthesised_speech: | |
| synthesised_speech_data = synthesised_speech['audio'] | |
| else: | |
| print("The synthesised speech does not contain 'audio'") | |
| return | |
| # Flatten the audio data | |
| synthesised_speech_data = synthesised_speech_data.flatten() | |
| # Scale the audio data to the range of int16 format | |
| synthesised_speech = (synthesised_speech_data * 32767).astype(np.int16) | |
| return 16000, synthesised_speech | |
| # Define the Gradio interface | |
| iface = gr.Interface( | |
| fn=translate_speech, | |
| inputs=gr.inputs.Audio(type="filepath"), # Change this line | |
| 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() | |