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DocVoice.py

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+import pyttsx3
+
+def text_to_speech(text: str):
+    # Initialize engine
+    engine = pyttsx3.init()
+
+    # Use default voice
+    engine.setProperty('voice', engine.getProperty('voices')[0].id)
+
+    # Speak the text
+    engine.say(text)
+    engine.runAndWait()
+
+# Example usage
+if __name__ == "__main__":
+    text_to_speech("Hello Abdul Moiz! This is your Riaya Tech project speaking.")

PaitentVoiceToText.py

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+# stt.py
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+import sounddevice as sd
+import numpy as np
+import scipy.io.wavfile as wav
+
+save_dir = r"C:\Users\JAY\Downloads\model\OpenAIWhisper"
+
+# Detect GPU
+use_cuda = torch.cuda.is_available()
+device_index = 0 if use_cuda else -1
+device_str = "cuda" if use_cuda else "cpu"
+dtype = torch.float16 if use_cuda else torch.float32
+
+# Load model
+try:
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        save_dir,
+        torch_dtype=dtype,
+        low_cpu_mem_usage=True,
+        use_safetensors=True,
+        local_files_only=True
+    ).to(device_str)
+    processor = AutoProcessor.from_pretrained(save_dir, local_files_only=True)
+except Exception as e:
+    print("Warning: Local model load failed, falling back to online model:", e)
+    hub_id = "openai/whisper-small"
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        hub_id,
+        torch_dtype=dtype,
+        low_cpu_mem_usage=True,
+        use_safetensors=True,
+    ).to(device_str)
+    processor = AutoProcessor.from_pretrained(hub_id)
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    torch_dtype=dtype,
+    device=device_index
+)
+
+print("Whisper pipeline ready.")
+
+
+def record_and_transcribe(duration=5, samplerate=16000, filename="mic_input.wav") -> str:
+    """
+    Record audio from the microphone, save it as a WAV file,
+    and return the transcribed text using Whisper.
+    """
+    # 1️⃣ Record audio
+    print(f"🎙️ Recording for {duration} seconds...")
+    audio = sd.rec(int(duration * samplerate), samplerate=samplerate, channels=1, dtype="float32")
+    sd.wait()
+    audio = np.squeeze(audio)
+
+    # 2️⃣ Save as WAV
+    wav.write(filename, samplerate, (audio * 32767).astype(np.int16))
+    print(f"✅ Recording saved as {filename}")
+
+    # 3️⃣ Transcribe
+    result = pipe(filename)
+    text = result["text"]
+    print(f"📝 Transcribed text: {text}")
+
+    return text
+