feat: add speech-to-text transcription functionality using Hugging Face ASR pipeline

src/speech_to_text.py

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+from transformers import pipeline
+import librosa # Or soundfile
+import os
+
+# Initialize the ASR pipeline with a specific model
+# Using a smaller Whisper model for quicker setup, but larger models offer better accuracy
+asr_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-tiny.en")
+
+def transcribe_audio(audio_filepath):
+    """
+    Transcribes an audio file using the Hugging Face ASR pipeline.
+    """
+    try:
+        transcription = asr_pipeline(audio_filepath)
+        return transcription["text"]
+    except Exception as e:
+        return f"Error during transcription: {e}"
+
+# Example usage:
+if __name__ == "__main__":
+    audio_file = "./downloaded_files/99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3.mp3"
+
+    if os.path.exists(audio_file): # Check if the (placeholder or real) file exists
+        print(f"Attempting to transcribe: {audio_file}")
+        transcribed_text = transcribe_audio(audio_file)
+        print(f"Transcription: {transcribed_text}")
+    else:
+        print(f"File not found: {audio_file}. Please provide a valid audio file.")
+
+    # To integrate this into your FileIdentifier or a subsequent processing step:
+    # identified_file_info = identifier.identify_file("path/to/your/audio.mp3")
+    # if identified_file_info.get("determined_type") == "audio":
+    #     text_from_audio = transcribe_audio(identified_file_info["filepath"])
+    #     print(f"Processed audio: {text_from_audio}")
+    # else:
+    #     print("Not an audio file or error in identification.")