"""Choosing hardware"""
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if device == "cuda":
print(
"Using GPU. Setting default tensor type to torch.cuda.FloatTensor")
torch.set_default_tensor_type("torch.cuda.FloatTensor")
else:
print("Using CPU. Setting default tensor type to torch.FloatTensor")
torch.set_default_tensor_type("torch.FloatTensor")
"""Converting model to specified hardware and format"""
acoustic_cfg_json = json.load(
open(args.acoustic_model.replace(".torch", ".json"), "r"))
acoustic_cfg = AcousticConfig.from_json(acoustic_cfg_json)
acoustic_model = CNN(acoustic_cfg)
acoustic_model.float().to(device)
try:
acoustic_model.load_state_dict(torch.load(args.acoustic_model))
except:
print(
"Failed to load model from {} without device mapping. Trying to load with mapping to {}"
.format(args.acoustic_model, device))
acoustic_model.load_state_dict(
torch.load(args.acoustic_model, map_location=device))
linguistic_cfg_json = json.load(
open(args.linguistic_model.replace(".torch", ".json"), "r"))
linguistic_cfg = LinguisticConfig.from_json(linguistic_cfg_json)
linguistic_model = AttentionModel(linguistic_cfg)
linguistic_model.float().to(device)
write(TMP_FILENAME, SAMPLE_RATE, myrecording) # Save as WAV file
from deepspeech_generator import speech_to_text
transcription = speech_to_text(join(args.deepspeech, "output_graph.pbmm"),
join(args.deepspeech, "alphabet.txt"),
join(args.deepspeech, "lm.binary"),
join(args.deepspeech, "trie"), TMP_FILENAME)
print(transcription)
"""Converting model to specified hardware and format"""
acoustic_cfg_json = json.load(
open(args.acoustic_model.replace(".torch", ".json"), "r"))
acoustic_cfg = AcousticSpectrogramConfig.from_json(acoustic_cfg_json)
acoustic_model = CNN(acoustic_cfg)
acoustic_model.float().to("cpu")
try:
acoustic_model.load_state_dict(torch.load(args.acoustic_model))
except:
print(
"Failed to load model from {} without device mapping. Trying to load with mapping to {}"
.format(args.acoustic_model, "cpu"))
acoustic_model.load_state_dict(
torch.load(args.acoustic_model, map_location="cpu"))
acoustic_model.eval()
linguistic_cfg_json = json.load(
open(args.linguistic_model.replace(".torch", ".json"), "r"))
linguistic_cfg = LinguisticConfig.from_json(linguistic_cfg_json)
