Update app.py

app.py

@@ -1,4 +1,4 @@
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, UploadFile, File
 from fastapi.responses import JSONResponse, FileResponse
 from pydantic import BaseModel
 import numpy as np
@@ -10,12 +10,13 @@ import torch
 import librosa
 from transformers import Wav2Vec2ForCTC, AutoProcessor
 from pathlib import Path
+from moviepy.editor import VideoFileClip
+import magic  # For MIME type detection
 
 # Import functions from other modules
 from asr import transcribe, ASR_LANGUAGES
 from tts import synthesize, TTS_LANGUAGES
 from lid import identify
-
 from asr import ASR_SAMPLING_RATE, transcribe
 
 # Configure logging
@@ -26,7 +27,7 @@ app = FastAPI(title="MMS: Scaling Speech Technology to 1000+ languages")
 
 # Define request models
 class AudioRequest(BaseModel):
-    audio: str  # Base64 encoded audio data
+    audio: str  # Base64 encoded audio or video data
     language: str
 
 class TTSRequest(BaseModel):
@@ -34,19 +35,42 @@ class TTSRequest(BaseModel):
     language: str
     speed: float
 
+def detect_mime_type(input_bytes):
+    mime = magic.Magic(mime=True)
+    return mime.from_buffer(input_bytes)
+
+def extract_audio(input_bytes):
+    mime_type = detect_mime_type(input_bytes)
+    
+    if mime_type.startswith('audio/'):
+        return sf.read(io.BytesIO(input_bytes))
+    elif mime_type.startswith('video/'):
+        with io.BytesIO(input_bytes) as f:
+            video = VideoFileClip(f.name)
+            audio = video.audio
+            audio_array = audio.to_soundarray()
+            sample_rate = audio.fps
+            return audio_array, sample_rate
+    else:
+        raise ValueError(f"Unsupported MIME type: {mime_type}")
+
 @app.post("/transcribe")
 async def transcribe_audio(request: AudioRequest):
     try:
-        audio_bytes = base64.b64decode(request.audio)
-        audio_array, sample_rate = sf.read(io.BytesIO(audio_bytes))
+        input_bytes = base64.b64decode(request.audio)
+        audio_array, sample_rate = extract_audio(input_bytes)
+
         # Convert to mono if stereo
         if len(audio_array.shape) > 1:
             audio_array = audio_array.mean(axis=1)
+
         # Ensure audio_array is float32
         audio_array = audio_array.astype(np.float32)
+
         # Resample if necessary
         if sample_rate != ASR_SAMPLING_RATE:
             audio_array = librosa.resample(audio_array, orig_sr=sample_rate, target_sr=ASR_SAMPLING_RATE)
+
         result = transcribe(audio_array, request.language)
         return JSONResponse(content={"transcription": result})
     except Exception as e:
@@ -57,15 +81,13 @@ async def transcribe_audio(request: AudioRequest):
 async def synthesize_speech(request: TTSRequest):
     try:
         audio, filtered_text = synthesize(request.text, request.language, request.speed)
-        
         # Convert numpy array to bytes
         buffer = io.BytesIO()
         sf.write(buffer, audio, 22050, format='wav')
         buffer.seek(0)
-        
         return FileResponse(
-            buffer, 
-            media_type="audio/wav", 
+            buffer,
+            media_type="audio/wav",
             headers={"Content-Disposition": "attachment; filename=synthesized_audio.wav"}
         )
     except Exception as e:
@@ -75,9 +97,8 @@ async def synthesize_speech(request: TTSRequest):
 @app.post("/identify")
 async def identify_language(request: AudioRequest):
     try:
-        audio_bytes = base64.b64decode(request.audio)
-        audio_array, sample_rate = sf.read(io.BytesIO(audio_bytes))
-        
+        input_bytes = base64.b64decode(request.audio)
+        audio_array, sample_rate = extract_audio(input_bytes)
         result = identify(audio_array)
         return JSONResponse(content={"language_identification": result})
     except Exception as e: