Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load Hugging Face models for speech-to-text and grammar correction
+# For different languages, you can modify the pipelines to use language-specific models.
+s2t_en = pipeline("automatic-speech-recognition", model="facebook/s2t-medium-librispeech-asr")
+s2t_fr = pipeline("automatic-speech-recognition", model="facebook/s2t-medium-librispeech-asr")  # For French
+s2t_ur = pipeline("automatic-speech-recognition", model="facebook/s2t-medium-librispeech-asr")  # For Urdu (if available)
+
+grammar_en = pipeline("text2text-generation", model="prithivida/grammar_error_correcter_v1")
+grammar_fr = pipeline("text2text-generation", model="prithivida/grammar_error_correcter_v1")  # For French grammar correction
+grammar_ur = pipeline("text2text-generation", model="prithivida/grammar_error_correcter_v1")  # For Urdu grammar correction
+
+def out(audio1, audio2, input_lang, output_lang):
+    if input_lang == "English":
+        s2t_model = s2t_en
+        grammar_model = grammar_en
+    elif input_lang == "French":
+        s2t_model = s2t_fr
+        grammar_model = grammar_fr
+    else:
+        s2t_model = s2t_ur
+        grammar_model = grammar_ur
+
+    # Check if audio is provided
+    if audio1 is None and audio2 is None:
+        return "No audio uploaded", "No audio uploaded"
+    elif audio1 is None:
+        # Use the second audio input (microphone or file)
+        x = s2t_model(audio2)["text"]
+        corrected = grammar_model(x)[0]['generated_text']
+    else:
+        # Use the first audio input (microphone or file)
+        x = s2t_model(audio1)["text"]
+        corrected = grammar_model(x)[0]['generated_text']
+
+    # If output language is different, translate (you can use Hugging Face models for translation)
+    if output_lang == "English":
+        # Placeholder translation model; you should replace this with a suitable translation pipeline
+        # Currently, we are assuming output will be in the same language.
+        translated = corrected
+    elif output_lang == "French":
+        # Placeholder translation model for French
+        translated = corrected
+    else:
+        # Placeholder translation model for Urdu
+        translated = corrected
+
+    return corrected, translated
+
+
+# Define Gradio Interface
+iface = gr.Interface(
+    fn=out, 
+    title="Speech-to-Text with Grammar Correction and Translation",
+    description="Select input and output language. Upload an audio file or use the microphone to convert speech to text, correct the grammar, and optionally translate it.",
+    inputs=[
+        gr.inputs.Audio(source="upload", type="filepath", label="Upload Audio File (Optional)", optional=True),                                           
+        gr.inputs.Audio(source="microphone", type="filepath", label="Record Using Microphone (Optional)", optional=True),
+        gr.inputs.Dropdown(["English", "French", "Urdu"], label="Input Language", default="English"),
+        gr.inputs.Dropdown(["English", "French", "Urdu"], label="Output Language", default="English")
+    ],
+    outputs=["text", "text"],
+    examples=[["Grammar-Correct-Sample.mp3"], ["Grammar-Wrong-Sample.mp3"]],
+)
+
+# Launch Gradio Interface
+iface.launch(enable_queue=True, show_error=True)