Update app.py

app.py

@@ -1,17 +1,17 @@
 import gradio as gr
 import requests
-import soundfile as sf
 import numpy as np
-import tempfile
 from pydub import AudioSegment
 import io
 
 # Define the Hugging Face Inference API URLs and headers
-ASR_API_URL = "https://api-inference.huggingface.co/models/Baghdad99/saad-speech-recognition-hausa-audio-to-text"
 TTS_API_URL = "https://api-inference.huggingface.co/models/Baghdad99/english_voice_tts"
 TRANSLATION_API_URL = "https://api-inference.huggingface.co/models/Baghdad99/saad-hausa-text-to-english-text"
 headers = {"Authorization": "Bearer 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"}
 
+# Load the Gradio model for speech recognition
+asr_model = gr.load("models/Baghdad99/saad-speech-recognition-hausa-audio-to-text")
+
 # Define the function to query the Hugging Face Inference API
 def query(api_url, payload):
     response = requests.post(api_url, headers=headers, json=payload)
@@ -21,18 +21,8 @@ def query(api_url, payload):
 def translate_speech(audio_file):
     print(f"Type of audio: {type(audio_file)}, Value of audio: {audio_file}")  # Debug line
 
-    # Use the ASR pipeline to transcribe the audio
-    with open(audio_file.name, "rb") as f:  # Change this line
-        data = f.read()
-    response = requests.post(ASR_API_URL, headers=headers, data=data)
-    output = response.json()
-
-    # Check if the output contains 'text'
-    if 'text' in output:
-        transcription = output["text"]
-    else:
-        print("The output does not contain 'text'")
-        return
+    # Use the ASR model to transcribe the audio
+    transcription = asr_model.predict(audio_file.name)  # Change this line
 
     # Use the translation pipeline to translate the transcription
     translated_text = query(TRANSLATION_API_URL, {"inputs": transcription})