Spaces:
Sleeping
Sleeping
File size: 3,034 Bytes
b5fadc4 69067ae 7467739 b5fadc4 9061ed1 0197ed3 69067ae 1547e12 7494646 1547e12 7494646 1547e12 7467739 69067ae 7467739 b5fadc4 7467739 adb5e2a 7467739 685e8d2 8994492 b5fadc4 8994492 b5fadc4 9061ed1 8994492 b5fadc4 9061ed1 1547e12 a3e60d6 69067ae 7467739 69067ae 7467739 69067ae 7467739 8ab530a 69067ae b5fadc4 8ab530a 7494646 8994492 b5fadc4 8994492 b5fadc4 8994492 b5fadc4 9061ed1 7467739 7494646 9061ed1 0197ed3 8994492 69067ae 9061ed1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
import torch
from flask import Flask, render_template, request, jsonify
import os
from transformers import pipeline
from gtts import gTTS
from pydub import AudioSegment
from pydub.silence import detect_nonsilent
from waitress import serve
app = Flask(__name__)
# Use whisper-small for faster processing and better speed
device = "cuda" if torch.cuda.is_available() else "cpu"
asr_model = pipeline("automatic-speech-recognition", model="openai/whisper-small", device=0 if device == "cuda" else -1)
# Function to generate audio prompts
def generate_audio_prompt(text, filename):
tts = gTTS(text=text, lang="en")
tts.save(os.path.join("static", filename))
# Generate required voice prompts
prompts = {
"welcome": "Welcome to Biryani Hub.",
"ask_name": "Tell me your name.",
"ask_email": "Please provide your email address.",
"thank_you": "Thank you for registration."
}
for key, text in prompts.items():
generate_audio_prompt(text, f"{key}.mp3")
# Symbol mapping for proper recognition
SYMBOL_MAPPING = {
"at the rate": "@",
"at": "@",
"dot": ".",
"underscore": "_",
"hash": "#",
"plus": "+",
"dash": "-",
"comma": ",",
"space": " "
}
# Function to convert audio to WAV format
def convert_to_wav(input_path, output_path):
try:
audio = AudioSegment.from_file(input_path)
audio = audio.set_frame_rate(16000).set_channels(1) # Convert to 16kHz, mono
audio.export(output_path, format="wav")
except Exception as e:
raise Exception(f"Audio conversion failed: {str(e)}")
# Function to check if audio contains actual speech
def is_silent_audio(audio_path):
audio = AudioSegment.from_wav(audio_path)
nonsilent_parts = detect_nonsilent(audio, min_silence_len=500, silence_thresh=audio.dBFS-16) # Reduced silence duration
return len(nonsilent_parts) == 0 # If no speech detected
@app.route("/")
def index():
return render_template("index.html")
@app.route("/transcribe", methods=["POST"])
def transcribe():
if "audio" not in request.files:
return jsonify({"error": "No audio file provided"}), 400
audio_file = request.files["audio"]
input_audio_path = os.path.join("static", "temp_input.wav")
output_audio_path = os.path.join("static", "temp.wav")
audio_file.save(input_audio_path)
try:
# Convert to WAV
convert_to_wav(input_audio_path, output_audio_path)
# Check for silence
if is_silent_audio(output_audio_path):
return jsonify({"error": "No speech detected. Please try again."}), 400
# Use Whisper ASR model for transcription
result = asr_model(output_audio_path, generate_kwargs={"language": "en"})
transcribed_text = result["text"].strip().capitalize()
return jsonify({"text": transcribed_text})
except Exception as e:
return jsonify({"error": f"Speech recognition error: {str(e)}"}), 500
# Start Production Server
if __name__ == "__main__":
serve(app, host="0.0.0.0", port=7860)
|