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| """ | |
| Script to translate given single english audio file to corresponding hindi text | |
| Usage : python s2t_en2hi.py <audio_file_path> <averaged_checkpoints_file_path> | |
| """ | |
| import gradio as gr | |
| import sys | |
| import os | |
| import subprocess | |
| from pydub import AudioSegment | |
| def install_fairseq(): | |
| try: | |
| # Run pip install command to install fairseq | |
| subprocess.check_call(["pip", "install", "fairseq"]) | |
| subprocess.check_call(["pip", "install", "sentencepiece"]) | |
| subprocess.check_call(["pip", "install", "soundfile"]) | |
| return "fairseq successfully installed!" | |
| except subprocess.CalledProcessError as e: | |
| return f"An error occurred while installing fairseq: {str(e)}" | |
| def convert_audio_to_16k_wav(audio_input): | |
| sound = AudioSegment.from_file(audio_input) | |
| sample_rate = sound.frame_rate | |
| num_channels = sound.channels | |
| num_frames = int(sound.frame_count()) | |
| filename = audio_input.split("/")[-1] | |
| print("original file is at:", audio_input) | |
| if (num_channels > 1) or (sample_rate != 16000): # convert to mono-channel 16k wav | |
| if num_channels > 1: | |
| sound = sound.set_channels(1) | |
| if sample_rate != 16000: | |
| sound = sound.set_frame_rate(16000) | |
| num_frames = int(sound.frame_count()) | |
| filename = filename.replace(".wav", "") + "_16k.wav" | |
| sound.export(f"{filename}", format="wav") | |
| return filename | |
| def run_my_code(input_text, language): | |
| # TODO better argument handling | |
| audio=convert_audio_to_16k_wav(input_text) | |
| hi_wav = audio | |
| data_root="" | |
| model_checkpoint="" | |
| d_r="" | |
| if(language=="Hindi"): | |
| model_checkpoint = "./models/hindi_model.pt" | |
| data_root="./MUSTC_ROOT_hindi/en-hi/" | |
| d_r="MUSTC_ROOT_hindi/" | |
| if(language=="French"): | |
| model_checkpoint = "./models/french_model.pt" | |
| data_root="./MUSTC_ROOT_french/en-fr/" | |
| d_r="MUSTC_ROOT_french/" | |
| os.system(f"cp {hi_wav} {data_root}data/tst-COMMON/wav/test.wav") | |
| print("------Starting data prepration...") | |
| subprocess.run(["python", "prep_mustc_data_hindi_single.py", "--data-root", d_r, "--task", "st", "--vocab-type", "unigram", "--vocab-size", "8000"], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) | |
| print("------Performing translation...") | |
| translation_result = subprocess.run(["python", "generate.py", data_root, "--config-yaml", "config_st.yaml", "--gen-subset", "tst-COMMON_st", "--task", "speech_to_text", "--path", model_checkpoint], capture_output=True, text=True) | |
| translation_result_text = translation_result.stdout | |
| lines = translation_result_text.split("\n") | |
| output_text="" | |
| print("\n\n------Translation results are:") | |
| for i in lines: | |
| if (i.startswith("D-0")): | |
| print(i.split("\t")[2]) | |
| output_text=i.split("\t")[2] | |
| break | |
| os.system(f"rm {data_root}data/tst-COMMON/wav/test.wav") | |
| return output_text | |
| install_fairseq() | |
| # Define the input and output interfaces for Gradio | |
| #inputs = [ | |
| # gr.inputs.Audio(source="microphone", type="filepath", label="Record something (in English)..."), | |
| # gr.inputs.Dropdown(list(LANGUAGE_CODES.keys()), default="Hindi", label="From English to Languages X..."), | |
| # ] | |
| input_textbox = gr.inputs.Textbox(label="test2.wav") | |
| #input=gr.inputs.Audio(source="microphone", type="filepath", label="Record something (in English)...") | |
| #audio=convert_audio_to_16k_wav(input) | |
| output_textbox = gr.outputs.Textbox(label="Output Text") | |
| # Create a Gradio interface | |
| iface = gr.Interface( | |
| fn=run_my_code, | |
| inputs=[input_textbox, gr.inputs.Radio(["Hindi", "French"], label="Language")], | |
| outputs=output_textbox, | |
| title="English to Hindi Translator") | |
| # Launch the interface | |
| iface.launch() |