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# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from huggingface_hub import hf_hub_download
from functools import lru_cache
from offline_asr import OfflineAsr
sample_rate = 16000
@lru_cache(maxsize=1)
def get_gigaspeech_pre_trained_model():
nn_model_filename = hf_hub_download(
# It is converted from https://huggingface.co/wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2
repo_id="csukuangfj/icefall-asr-gigaspeech-pruned-transducer-stateless2",
filename="cpu_jit-epoch-29-avg-11-torch-1.10.0.pt",
subfolder="exp",
)
bpe_model_filename = hf_hub_download(
repo_id="wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2",
filename="bpe.model",
subfolder="data/lang_bpe_500",
)
return OfflineAsr(
nn_model_filename=nn_model_filename,
bpe_model_filename=bpe_model_filename,
token_filename=None,
decoding_method="greedy_search",
num_active_paths=4,
sample_rate=sample_rate,
device="cpu",
)
@lru_cache(maxsize=1)
def get_wenetspeech_pre_trained_model():
nn_model_filename = hf_hub_download(
repo_id="luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2",
filename="cpu_jit_epoch_10_avg_2_torch_1.7.1.pt",
subfolder="exp",
)
token_filename = hf_hub_download(
repo_id="luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2",
filename="tokens.txt",
subfolder="data/lang_char",
)
return OfflineAsr(
nn_model_filename=nn_model_filename,
bpe_model_filename=None,
token_filename=token_filename,
decoding_method="greedy_search",
num_active_paths=4,
sample_rate=sample_rate,
device="cpu",
)
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