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import torch
import torchaudio # ✅ Added torchaudio to handle audio resampling
import gradio as gr
import time
import numpy as np
import scipy.io.wavfile
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
# ✅ 1️⃣ Force Model to Run on CPU
device = "cpu"
torch_dtype = torch.float32 # Use CPU-friendly float type
MODEL_NAME = "openai/whisper-tiny" # ✅ Switched to smallest model for fastest performance
# ✅ 2️⃣ Load Whisper Tiny Model on CPU (Removed `low_cpu_mem_usage=True`)
model = AutoModelForSpeechSeq2Seq.from_pretrained(
MODEL_NAME, torch_dtype=torch_dtype, use_safetensors=True # ✅ Removed low_cpu_mem_usage
)
model.to(device)
# ✅ 3️⃣ Load Processor & Pipeline
processor = AutoProcessor.from_pretrained(MODEL_NAME)
pipe = pipeline(
task="automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
chunk_length_s=2, # ✅ Process in 2-second chunks for ultra-low latency
torch_dtype=torch_dtype,
device=device,
sampling_rate=16000, # ✅ Explicitly set sampling rate to avoid resampling issues
)
# ✅ 4️⃣ Real-Time Streaming Transcription (Microphone)
def stream_transcribe(stream, new_chunk):
start_time = time.time()
try:
sr, y = new_chunk
# ✅ Convert stereo to mono
if y.ndim > 1:
y = y.mean(axis=1)
y = y.astype(np.float32)
y /= np.max(np.abs(y))
# ✅ Resample audio
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