Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel
+import soundfile as sf
+
+# Load the model and processor
+model = SpeechEncoderDecoderModel.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
+processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
+
+# Define the transcription function
+def transcribe_speech(file_info):
+    # Read the audio file
+    speech, _ = sf.read(file_info)
+    # Process the speech
+    inputs = processor(speech, sampling_rate=16_000, return_tensors="pt")
+    # Generate the transcription
+    generated_ids = model.generate(inputs=inputs["input_values"], attention_mask=inputs["attention_mask"])
+    # Decode the generated ids to text
+    transcription = processor.batch_decode(generated_ids)
+
+    return transcription[0]
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=transcribe_speech,
+    inputs=gr.inputs.Audio(source="upload", type="filepath", label="Upload your MP3 file"),
+    outputs="text",
+    title="Speech to Text Conversion",
+    description="Upload an MP3 file to transcribe it to text using a state-of-the-art speech-to-text model."
+)
+
+# Run the Gradio app
+iface.launch()