Use NVIDIA Canary-1B-v2 for ASR, Translation, and Computerized SRT Subtitle Export in Python

import time, json, gc, math, urllib.request
import torch, numpy as np, soundfile as sf, librosa
print(">>> PHASE 2: operating tutorialn")
print("NumPy:", np.__version__, "| PyTorch:", torch.__version__)
print("CUDA out there:", torch.cuda.is_available())
if torch.cuda.is_available():
   print("GPU:", torch.cuda.get_device_name(0),
         f"| VRAM: {torch.cuda.get_device_properties(0).total_memory/1e9:.1f} GB")
else:
   print("⚠️  No GPU — will run on CPU (very gradual). "
         "Set Runtime > Change runtime sort > GPU.")
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
LANGS = {
   "bg":"Bulgarian","hr":"Croatian","cs":"Czech","da":"Danish","nl":"Dutch",
   "en":"English","et":"Estonian","fi":"Finnish","fr":"French","de":"German",
   "el":"Greek","hu":"Hungarian","it":"Italian","lv":"Latvian","lt":"Lithuanian",
   "mt":"Maltese","pl":"Polish","pt":"Portuguese","ro":"Romanian","sk":"Slovak",
   "sl":"Slovenian","es":"Spanish","sv":"Swedish","ru":"Russian","uk":"Ukrainian",
}
print(f"nSupported languages ({len(LANGS)}):", ", ".be a part of(LANGS.keys()))
from nemo.collections.asr.fashions import ASRModel
print("nLoading nvidia/canary-1b-v2 ...")
t0 = time.time()
asr_model = ASRModel.from_pretrained(model_name="nvidia/canary-1b-v2").to(DEVICE).eval()
print(f"Mannequin loaded in {time.time()-t0:.1f}s")