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# Copyright 2022-2023 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 functools import lru_cache
import sherpa_onnx
from huggingface_hub import hf_hub_download
sample_rate = 16000
def _get_nn_model_filename(
repo_id: str,
filename: str,
subfolder: str = "exp",
) -> str:
nn_model_filename = hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder,
)
return nn_model_filename
def _get_bpe_model_filename(
repo_id: str,
filename: str = "bpe.model",
subfolder: str = "data/lang_bpe_500",
) -> str:
bpe_model_filename = hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder,
)
return bpe_model_filename
def _get_token_filename(
repo_id: str,
filename: str = "tokens.txt",
subfolder: str = "data/lang_char",
) -> str:
token_filename = hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder,
)
return token_filename
@lru_cache(maxsize=10)
def _get_whisper_model(repo_id: str) -> sherpa_onnx.OfflineRecognizer:
name = repo_id.split("-")[1]
assert name in ("tiny.en", "base.en", "small.en", "medium.en"), repo_id
full_repo_id = "csukuangfj/sherpa-onnx-whisper-" + name
encoder = _get_nn_model_filename(
repo_id=full_repo_id,
filename=f"{name}-encoder.int8.ort",
subfolder=".",
)
decoder = _get_nn_model_filename(
repo_id=full_repo_id,
filename=f"{name}-decoder.int8.ort",
subfolder=".",
)
tokens = _get_token_filename(
repo_id=full_repo_id, subfolder=".", filename=f"{name}-tokens.txt"
)
recognizer = sherpa_onnx.OfflineRecognizer.from_whisper(
encoder=encoder,
decoder=decoder,
tokens=tokens,
num_threads=2,
)
return recognizer
@lru_cache(maxsize=10)
def _get_paraformer_zh_pre_trained_model(repo_id: str) -> sherpa_onnx.OfflineRecognizer:
assert repo_id in [
"csukuangfj/sherpa-onnx-paraformer-zh-2023-03-28",
], repo_id
nn_model = _get_nn_model_filename(
repo_id=repo_id,
filename="model.int8.onnx",
subfolder=".",
)
tokens = _get_token_filename(repo_id=repo_id, subfolder=".")
recognizer = sherpa_onnx.OfflineRecognizer.from_paraformer(
paraformer=nn_model,
tokens=tokens,
num_threads=2,
sample_rate=sample_rate,
feature_dim=80,
decoding_method="greedy_search",
debug=False,
)
return recognizer
@lru_cache(maxsize=10)
def _get_russian_pre_trained_model(repo_id: str) -> sherpa_onnx.OfflineRecognizer:
assert repo_id in (
"alphacep/vosk-model-ru",
"alphacep/vosk-model-small-ru",
), repo_id
if repo_id == "alphacep/vosk-model-ru":
model_dir = "am-onnx"
elif repo_id == "alphacep/vosk-model-small-ru":
model_dir = "am"
encoder_model = _get_nn_model_filename(
repo_id=repo_id,
filename="encoder.onnx",
subfolder=model_dir,
)
decoder_model = _get_nn_model_filename(
repo_id=repo_id,
filename="decoder.onnx",
subfolder=model_dir,
)
joiner_model = _get_nn_model_filename(
repo_id=repo_id,
filename="joiner.onnx",
subfolder=model_dir,
)
tokens = _get_token_filename(repo_id=repo_id, subfolder="lang")
recognizer = sherpa_onnx.OfflineRecognizer.from_transducer(
tokens=tokens,
encoder=encoder_model,
decoder=decoder_model,
joiner=joiner_model,
num_threads=2,
sample_rate=16000,
feature_dim=80,
decoding_method="greedy_search",
)
return recognizer
@lru_cache(maxsize=2)
def get_vad() -> sherpa_onnx.VoiceActivityDetector:
vad_model = _get_nn_model_filename(
repo_id="csukuangfj/vad",
filename="silero_vad.onnx",
subfolder=".",
)
config = sherpa_onnx.VadModelConfig()
config.silero_vad.model = vad_model
config.silero_vad.min_silence_duration = 0.15
config.silero_vad.min_speech_duration = 0.25
config.sample_rate = sample_rate
vad = sherpa_onnx.VoiceActivityDetector(
config,
buffer_size_in_seconds=180,
)
return vad
@lru_cache(maxsize=10)
def get_pretrained_model(repo_id: str) -> sherpa_onnx.OfflineRecognizer:
if repo_id in english_models:
return english_models[repo_id](repo_id)
elif repo_id in chinese_english_mixed_models:
return chinese_english_mixed_models[repo_id](repo_id)
elif repo_id in russian_models:
return russian_models[repo_id](repo_id)
else:
raise ValueError(f"Unsupported repo_id: {repo_id}")
english_models = {
"whisper-tiny.en": _get_whisper_model,
"whisper-base.en": _get_whisper_model,
"whisper-small.en": _get_whisper_model,
}
chinese_english_mixed_models = {
"csukuangfj/sherpa-onnx-paraformer-zh-2023-03-28": _get_paraformer_zh_pre_trained_model,
}
russian_models = {
"alphacep/vosk-model-ru": _get_russian_pre_trained_model,
"alphacep/vosk-model-small-ru": _get_russian_pre_trained_model,
}
language_to_models = {
"English": list(english_models.keys()),
"Chinese+English": list(chinese_english_mixed_models.keys()),
"Russian": list(russian_models.keys()),
}
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