Create app.py

Initial code upload

app.py

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+from transformers import pipeline
+from transformers.utils import logging
+import torch
+import pandas as pd
+import time
+import gradio as gr
+
+logging.set_verbosity_error()
+
+asr = pipeline(task="automatic-speech-recognition",
+               model ='openai/whisper-large-v3')
+
+translator = pipeline(task="translation",
+                      model="facebook/nllb-200-3.3B", max_length = 5120,
+                      # model="facebook/nllb-200-distilled-600M",
+                      torch_dtype=torch.bfloat16)
+
+flores_200_df = pd.read_csv("Flores200_language_codes.csv", encoding='cp1252')
+flores_200 = dict(zip(flores_200_df['Language'],flores_200_df['FLORES_200_code']))
+flores_200_languages = list(flores_200.keys())
+
+def transcribe_audio(filepath, src_language, tgt_language):
+
+    target_language = flores_200_df.loc[int(tgt_language),'Language']
+    source_language = flores_200_df.loc[int(src_language),'Language']
+    print(f"Selected Source Language: {source_language}, Target Language: {target_language}")
+
+    time.sleep(5)
+    if filepath is None:
+        gr.Warning("No audio found, please retry.")
+        return ""
+
+    english_transcript = asr(
+      filepath,
+      # max_new_tokens=256,
+      chunk_length_s=30,
+      batch_size=8,
+    )['text']
+    print(english_transcript)
+
+    transcripts = english_transcript.split('.')
+
+    translations = []
+    for tscript in transcripts:
+      translation = translator(tscript, src_lang="eng_Latn",
+                            tgt_lang=flores_200_df.loc[int(tgt_language),'FLORES_200_code'])[0]['translation_text']
+      translations.append(translation+'.')
+
+    output = ' '.join(translations)LORES_200_code'])[0]['translation_text']
+
+    print(output)
+    return output
+
+demo = gr.Blocks()
+
+mic_transcribe = gr.Interface(title="Transcribe Audio of Any Language into Any Language - test and demo app by Srinivas.V ..",
+                              description="Speak into your system using your system mic, select your source & target languages and submit (if error appears, retry)",
+    fn=transcribe_audio,
+    inputs=[gr.Audio(sources="microphone",
+                    type="filepath"), gr.Dropdown(flores_200_df.Language.tolist(), type='index', label='Select Source Language'),
+                    gr.Dropdown(flores_200_df.Language.tolist(), type='index', label='Select Target Language')],
+    outputs=gr.Textbox(label="Transcription in Selected Target Language",
+                       lines=3),
+    allow_flagging="never")
+
+file_transcribe = gr.Interface(title="Transcribe Audio of Any Language into Any Language - test and demo app by Srinivas.V ..",
+                               description="Upload an audio file, select your source & target languages and submit (if error appears, retry)",
+    fn=transcribe_audio,
+    inputs=[gr.Audio(sources="upload",
+                    type="filepath"), gr.Dropdown(flores_200_df.Language.tolist(), type='index', label='Select Source Language'),
+                    gr.Dropdown(flores_200_df.Language.tolist(), type='index', label='Select Target Language')],
+    outputs=gr.Textbox(label="Transcription in Selected Target Language",
+                       lines=3),
+    allow_flagging="never",
+)
+
+with demo:
+    gr.TabbedInterface(
+        [mic_transcribe,
+         file_transcribe],
+        ["Speak Through Microphone",
+         "Upload Audio File"],
+    )
+demo.launch(debug=True)