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from transformers import pipeline |
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from transformers.utils import logging |
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import torch |
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import pandas as pd |
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import time |
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import gradio as gr |
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logging.set_verbosity_error() |
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asr = pipeline(task="automatic-speech-recognition", |
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model ='openai/whisper-large-v3') |
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translator = pipeline(task="translation", |
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model="facebook/nllb-200-3.3B", max_length = 5120, |
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torch_dtype=torch.bfloat16) |
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flores_200_df = pd.read_csv("Flores200_language_codes.csv", encoding='cp1252') |
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flores_200 = dict(zip(flores_200_df['Language'],flores_200_df['FLORES_200_code'])) |
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flores_200_languages = list(flores_200.keys()) |
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def transcribe_audio(filepath, src_language, tgt_language): |
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target_language = flores_200_df.loc[int(tgt_language),'Language'] |
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source_language = flores_200_df.loc[int(src_language),'Language'] |
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print(f"Selected Source Language: {source_language}, Target Language: {target_language}") |
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time.sleep(5) |
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if filepath is None: |
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gr.Warning("No audio found, please retry.") |
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return "" |
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english_transcript = asr( |
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filepath, |
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chunk_length_s=30, |
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batch_size=8, |
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)['text'] |
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print(english_transcript) |
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transcripts = english_transcript.split('.') |
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translations = [] |
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for tscript in transcripts: |
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translation = translator(tscript, src_lang="eng_Latn", |
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tgt_lang=flores_200_df.loc[int(tgt_language),'FLORES_200_code'])[0]['translation_text'] |
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translations.append(translation+'.') |
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output = ' '.join(translations) |
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print(output) |
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return output |
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demo = gr.Blocks() |
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mic_transcribe = gr.Interface(title="Transcribe Audio of Any Language into Any Language - test and demo app by Srinivas.V ..", |
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description="Speak into your system using your system mic, select your source & target languages and submit (if error appears, retry)", |
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fn=transcribe_audio, |
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inputs=[gr.Audio(sources="microphone", |
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type="filepath"), gr.Dropdown(flores_200_df.Language.tolist(), type='index', label='Select Source Language'), |
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gr.Dropdown(flores_200_df.Language.tolist(), type='index', label='Select Target Language')], |
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outputs=gr.Textbox(label="Transcription in Selected Target Language", |
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lines=3), |
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allow_flagging="never") |
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file_transcribe = gr.Interface(title="Transcribe Audio of Any Language into Any Language - test and demo app by Srinivas.V ..", |
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description="Upload an audio file, select your source & target languages and submit (if error appears, retry)", |
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fn=transcribe_audio, |
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inputs=[gr.Audio(sources="upload", |
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type="filepath"), gr.Dropdown(flores_200_df.Language.tolist(), type='index', label='Select Source Language'), |
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gr.Dropdown(flores_200_df.Language.tolist(), type='index', label='Select Target Language')], |
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outputs=gr.Textbox(label="Transcription in Selected Target Language", |
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lines=3), |
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allow_flagging="never", |
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) |
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with demo: |
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gr.TabbedInterface( |
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[mic_transcribe, |
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file_transcribe], |
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["Speak Through Microphone", |
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"Upload Audio File"], |
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) |
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demo.launch(debug=True) |
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