speech_recognition/FunASR
1
0
Fork 0
mirror of https://github.com/modelscope/FunASR synced 2025-09-15 14:48:36 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

update demo, readme

Browse Source
This commit is contained in:
shixian.shi 2024-01-16 11:30:25 +08:00
parent 948b68774c
commit b7cb19b01a
16 changed files with 63 additions and 70 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

24
README.md
View File

@ -95,9 +95,9 @@ model = AutoModel(model="paraformer-zh", model_revision="v2.0.2", \
vad_model="fsmn-vad", vad_model_revision="v2.0.2", \ vad_model="fsmn-vad", vad_model_revision="v2.0.2", \
punc_model="ct-punc-c", punc_model_revision="v2.0.2", \ punc_model="ct-punc-c", punc_model_revision="v2.0.2", \
spk_model="cam++", spk_model_revision="v2.0.2") spk_model="cam++", spk_model_revision="v2.0.2")
res = model(input=f"{model.model_path}/example/asr_example.wav", res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
batch_size=64, batch_size=64,
hotword='魔搭') hotword='魔搭')
print(res) print(res)
``` ```
Note: `model_hub`: represents the model repository, `ms` stands for selecting ModelScope download, `hf` stands for selecting Huggingface download. Note: `model_hub`: represents the model repository, `ms` stands for selecting ModelScope download, `hf` stands for selecting Huggingface download.
@ -124,7 +124,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num): for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride] speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1 is_final = i == total_chunk_num - 1
res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back) res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
print(res) print(res)
``` ```
Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `10*60=600ms`, and the lookahead information is `5*60=300ms`. Each inference input is `600ms` (sample points are `16000*0.6=960`), and the output is the corresponding text. For the last speech segment input, `is_final=True` needs to be set to force the output of the last word. Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `10*60=600ms`, and the lookahead information is `5*60=300ms`. Each inference input is `600ms` (sample points are `16000*0.6=960`), and the output is the corresponding text. For the last speech segment input, `is_final=True` needs to be set to force the output of the last word.
@ -135,7 +135,7 @@ from funasr import AutoModel
model = AutoModel(model="fsmn-vad", model_revision="v2.0.2") model = AutoModel(model="fsmn-vad", model_revision="v2.0.2")
wav_file = f"{model.model_path}/example/asr_example.wav" wav_file = f"{model.model_path}/example/asr_example.wav"
res = model(input=wav_file) res = model.generate(input=wav_file)
print(res) print(res)
``` ```
### Voice Activity Detection (Non-streaming) ### Voice Activity Detection (Non-streaming)
@ -156,7 +156,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num): for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride] speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1 is_final = i == total_chunk_num - 1
res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size) res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
if len(res[0]["value"]): if len(res[0]["value"]):
print(res) print(res)
``` ```
@ -165,7 +165,7 @@ for i in range(total_chunk_num):
from funasr import AutoModel from funasr import AutoModel
model = AutoModel(model="ct-punc", model_revision="v2.0.2") model = AutoModel(model="ct-punc", model_revision="v2.0.2")
res = model(input="那今天的会就到这里吧 happy new year 明年见") res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
print(res) print(res)
``` ```
### Timestamp Prediction ### Timestamp Prediction
@ -175,7 +175,7 @@ from funasr import AutoModel
model = AutoModel(model="fa-zh", model_revision="v2.0.2") model = AutoModel(model="fa-zh", model_revision="v2.0.2")
wav_file = f"{model.model_path}/example/asr_example.wav" wav_file = f"{model.model_path}/example/asr_example.wav"
text_file = f"{model.model_path}/example/text.txt" text_file = f"{model.model_path}/example/text.txt"
res = model(input=(wav_file, text_file), data_type=("sound", "text")) res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
print(res) print(res)
``` ```
[//]: # (FunASR supports inference and fine-tuning of models trained on industrial datasets of tens of thousands of hours. For more details, please refer to ([modelscope_egs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)). It also supports training and fine-tuning of models on academic standard datasets. For more details, please refer to([egs](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html)). The models include speech recognition (ASR), speech activity detection (VAD), punctuation recovery, language model, speaker verification, speaker separation, and multi-party conversation speech recognition. For a detailed list of models, please refer to the [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md):) [//]: # (FunASR supports inference and fine-tuning of models trained on industrial datasets of tens of thousands of hours. For more details, please refer to ([modelscope_egs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)). It also supports training and fine-tuning of models on academic standard datasets. For more details, please refer to([egs](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html)). The models include speech recognition (ASR), speech activity detection (VAD), punctuation recovery, language model, speaker verification, speaker separation, and multi-party conversation speech recognition. For a detailed list of models, please refer to the [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md):)
@ -229,10 +229,16 @@ The use of pretraining model is subject to [model license](./MODEL_LICENSE)
} }
@inproceedings{gao22b_interspeech, @inproceedings{gao22b_interspeech,
author={Zhifu Gao and ShiLiang Zhang and Ian McLoughlin and Zhijie Yan}, author={Zhifu Gao and ShiLiang Zhang and Ian McLoughlin and Zhijie Yan},
title={{Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition}}, title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
year=2022, year=2022,
booktitle={Proc. Interspeech 2022}, booktitle={Proc. Interspeech 2022},
pages={2063--2067}, pages={2063--2067},
doi={10.21437/Interspeech.2022-9996} doi={10.21437/Interspeech.2022-9996}
} }
@inproceedings{shi2023seaco,
author={Xian Shi and Yexin Yang and Zerui Li and Yanni Chen and Zhifu Gao and Shiliang Zhang},
title={SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability},
year={2023},
booktitle={ICASSP2024}
}
``` ```

18
README_zh.md
View File

@ -91,7 +91,7 @@ model = AutoModel(model="paraformer-zh", model_revision="v2.0.2", \
vad_model="fsmn-vad", vad_model_revision="v2.0.2", \ vad_model="fsmn-vad", vad_model_revision="v2.0.2", \
punc_model="ct-punc-c", punc_model_revision="v2.0.2", \ punc_model="ct-punc-c", punc_model_revision="v2.0.2", \
spk_model="cam++", spk_model_revision="v2.0.2") spk_model="cam++", spk_model_revision="v2.0.2")
res = model(input=f"{model.model_path}/example/asr_example.wav", res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
batch_size=64, batch_size=64,
hotword='魔搭') hotword='魔搭')
print(res) print(res)
@ -121,7 +121,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num): for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride] speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1 is_final = i == total_chunk_num - 1
res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back) res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
print(res) print(res)
``` ```
@ -134,7 +134,7 @@ from funasr import AutoModel
model = AutoModel(model="fsmn-vad", model_revision="v2.0.2") model = AutoModel(model="fsmn-vad", model_revision="v2.0.2")
wav_file = f"{model.model_path}/example/asr_example.wav" wav_file = f"{model.model_path}/example/asr_example.wav"
res = model(input=wav_file) res = model.generate(input=wav_file)
print(res) print(res)
``` ```
@ -156,7 +156,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num): for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride] speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1 is_final = i == total_chunk_num - 1
res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size) res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
if len(res[0]["value"]): if len(res[0]["value"]):
print(res) print(res)
``` ```
@ -167,7 +167,7 @@ from funasr import AutoModel
model = AutoModel(model="ct-punc", model_revision="v2.0.2") model = AutoModel(model="ct-punc", model_revision="v2.0.2")
res = model(input="那今天的会就到这里吧 happy new year 明年见") res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
print(res) print(res)
``` ```
@ -179,7 +179,7 @@ model = AutoModel(model="fa-zh", model_revision="v2.0.0")
wav_file = f"{model.model_path}/example/asr_example.wav" wav_file = f"{model.model_path}/example/asr_example.wav"
text_file = f"{model.model_path}/example/text.txt" text_file = f"{model.model_path}/example/text.txt"
res = model(input=(wav_file, text_file), data_type=("sound", "text")) res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
print(res) print(res)
``` ```
更多详细用法（[示例](examples/industrial_data_pretraining)） 更多详细用法（[示例](examples/industrial_data_pretraining)）
@ -242,4 +242,10 @@ FunASR支持预训练或者进一步微调的模型进行服务部署。目前
pages={2063--2067}, pages={2063--2067},
doi={10.21437/Interspeech.2022-9996} doi={10.21437/Interspeech.2022-9996}
} }
@article{shi2023seaco,
author={Xian Shi and Yexin Yang and Zerui Li and Yanni Chen and Zhifu Gao and Shiliang Zhang},
title={{SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability}},
year=2023,
journal={arXiv preprint arXiv:2308.03266(accepted by ICASSP2024)},
}
``` ```

16
examples/industrial_data_pretraining/bicif_paraformer/demo.py
View File

@ -6,14 +6,14 @@
from funasr import AutoModel from funasr import AutoModel
model = AutoModel(model="damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model = AutoModel(model="damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
model_revision="v2.0.2", model_revision="v2.0.2",
vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
vad_model_revision="v2.0.2", vad_model_revision="v2.0.2",
punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
punc_model_revision="v2.0.2", punc_model_revision="v2.0.2",
spk_model="damo/speech_campplus_sv_zh-cn_16k-common", spk_model="damo/speech_campplus_sv_zh-cn_16k-common",
spk_model_revision="v2.0.2", spk_model_revision="v2.0.2",
) )
res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_vad_punc_example.wav", batch_size_s=300, batch_size_threshold_s=60) res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_vad_punc_example.wav", batch_size_s=300, batch_size_threshold_s=60)
print(res) print(res)

2
examples/industrial_data_pretraining/campplus_sv/demo.py
View File

@ -9,5 +9,5 @@ model = AutoModel(model="damo/speech_campplus_sv_zh-cn_16k-common",
model_revision="v2.0.2", model_revision="v2.0.2",
) )
res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav") res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
print(res) print(res)

2
examples/industrial_data_pretraining/contextual_paraformer/demo.py
View File

@ -7,6 +7,6 @@ from funasr import AutoModel
model = AutoModel(model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404", model_revision="v2.0.2") model = AutoModel(model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404", model_revision="v2.0.2")
res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
hotword='达摩院 魔搭') hotword='达摩院 魔搭')
print(res) print(res)

4
examples/industrial_data_pretraining/ct_transformer/demo.py
View File

@ -7,7 +7,7 @@ from funasr import AutoModel
model = AutoModel(model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", model_revision="v2.0.2") model = AutoModel(model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", model_revision="v2.0.2")
res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt") res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
print(res) print(res)
@ -15,5 +15,5 @@ from funasr import AutoModel
model = AutoModel(model="damo/punc_ct-transformer_cn-en-common-vocab471067-large", model_revision="v2.0.2") model = AutoModel(model="damo/punc_ct-transformer_cn-en-common-vocab471067-large", model_revision="v2.0.2")
res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt") res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
print(res) print(res)

2
examples/industrial_data_pretraining/ct_transformer_streaming/demo.py
View File

@ -12,7 +12,7 @@ vads = inputs.split("|")
rec_result_all = "outputs: " rec_result_all = "outputs: "
cache = {} cache = {}
for vad in vads: for vad in vads:
rec_result = model(input=vad, cache=cache) rec_result = model.generate(input=vad, cache=cache)
print(rec_result) print(rec_result)
rec_result_all += rec_result[0]['text'] rec_result_all += rec_result[0]['text']

2
examples/industrial_data_pretraining/emotion2vec/demo.py
View File

@ -7,5 +7,5 @@ from funasr import AutoModel
model = AutoModel(model="damo/emotion2vec_base", model_revision="v2.0.1") model = AutoModel(model="damo/emotion2vec_base", model_revision="v2.0.1")
res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", output_dir="./outputs") res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", output_dir="./outputs")
print(res) print(res)

4
examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py
View File

@ -9,7 +9,7 @@ wav_file = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audi
chunk_size = 60000 # ms chunk_size = 60000 # ms
model = AutoModel(model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="v2.0.2") model = AutoModel(model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="v2.0.2")
res = model(input=wav_file, chunk_size=chunk_size, ) res = model.generate(input=wav_file, chunk_size=chunk_size, )
print(res) print(res)
@ -28,7 +28,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num): for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride] speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1 is_final = i == total_chunk_num - 1
res = model(input=speech_chunk, res = model.generate(input=speech_chunk,
cache=cache, cache=cache,
is_final=is_final, is_final=is_final,
chunk_size=chunk_size, chunk_size=chunk_size,

2
examples/industrial_data_pretraining/monotonic_aligner/demo.py
View File

@ -7,7 +7,7 @@ from funasr import AutoModel
model = AutoModel(model="damo/speech_timestamp_prediction-v1-16k-offline", model_revision="v2.0.2") model = AutoModel(model="damo/speech_timestamp_prediction-v1-16k-offline", model_revision="v2.0.2")
res = model(input=("https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", res = model.generate(input=("https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
"欢迎大家来到魔搭社区进行体验"), "欢迎大家来到魔搭社区进行体验"),
data_type=("sound", "text"), data_type=("sound", "text"),
batch_size=2, batch_size=2,

4
examples/industrial_data_pretraining/paraformer-zh-spk/demo.py
View File

@ -15,6 +15,6 @@ model = AutoModel(model="damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-co
spk_model_revision="v2.0.2" spk_model_revision="v2.0.2"
) )
res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
hotword='达摩院 磨搭') hotword='达摩院 磨搭')
print(res) print(res)

4
examples/industrial_data_pretraining/paraformer/demo.py
View File

@ -7,7 +7,7 @@ from funasr import AutoModel
model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revision="v2.0.2") model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revision="v2.0.2")
res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav") res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
print(res) print(res)
@ -18,5 +18,5 @@ frontend = AutoFrontend(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-co
fbanks = frontend(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", batch_size=2) fbanks = frontend(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", batch_size=2)
for batch_idx, fbank_dict in enumerate(fbanks): for batch_idx, fbank_dict in enumerate(fbanks):
res = model(**fbank_dict) res = model.generate(**fbank_dict)
print(res) print(res)

16
examples/industrial_data_pretraining/paraformer_streaming/demo.py
View File

@ -11,7 +11,7 @@ decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cr
model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", model_revision="v2.0.2") model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", model_revision="v2.0.2")
cache = {} cache = {}
res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
chunk_size=chunk_size, chunk_size=chunk_size,
encoder_chunk_look_back=encoder_chunk_look_back, encoder_chunk_look_back=encoder_chunk_look_back,
decoder_chunk_look_back=decoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back,
@ -32,11 +32,11 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num): for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride] speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1 is_final = i == total_chunk_num - 1
res = model(input=speech_chunk, res = model.generate(input=speech_chunk,
cache=cache, cache=cache,
is_final=is_final, is_final=is_final,
chunk_size=chunk_size, chunk_size=chunk_size,
encoder_chunk_look_back=encoder_chunk_look_back, encoder_chunk_look_back=encoder_chunk_look_back,
decoder_chunk_look_back=decoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back,
) )
print(res) print(res)

4
examples/industrial_data_pretraining/seaco_paraformer/demo.py
View File

@ -15,6 +15,6 @@ model = AutoModel(model="damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-co
spk_model_revision="v2.0.2", spk_model_revision="v2.0.2",
) )
res = model(input=f"{model.model_path}/example/asr_example.wav", res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
hotword='达摩院 魔搭') hotword='达摩院 魔搭')
print(res) print(res)

8
funasr/auto/auto_model.py
View File

@ -264,7 +264,7 @@ class AutoModel:
# step.1: compute the vad model # step.1: compute the vad model
self.vad_kwargs.update(cfg) self.vad_kwargs.update(cfg)
beg_vad = time.time() beg_vad = time.time()
res = self.generate(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg) res = self.inference(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg)
end_vad = time.time() end_vad = time.time()
print(f"time cost vad: {end_vad - beg_vad:0.3f}") print(f"time cost vad: {end_vad - beg_vad:0.3f}")
@ -316,7 +316,7 @@ class AutoModel:
batch_size_ms_cum = 0 batch_size_ms_cum = 0
end_idx = j + 1 end_idx = j + 1
speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx]) speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
results = self.generate(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg) results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
if self.spk_model is not None: if self.spk_model is not None:
all_segments = [] all_segments = []
# compose vad segments: [[start_time_sec, end_time_sec, speech], [...]] # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
@ -327,7 +327,7 @@ class AutoModel:
segments = sv_chunk(vad_segments) segments = sv_chunk(vad_segments)
all_segments.extend(segments) all_segments.extend(segments)
speech_b = [i[2] for i in segments] speech_b = [i[2] for i in segments]
spk_res = self.generate(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg) spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
results[_b]['spk_embedding'] = spk_res[0]['spk_embedding'] results[_b]['spk_embedding'] = spk_res[0]['spk_embedding']
beg_idx = end_idx beg_idx = end_idx
if len(results) < 1: if len(results) < 1:
@ -378,7 +378,7 @@ class AutoModel:
# step.3 compute punc model # step.3 compute punc model
if self.punc_model is not None: if self.punc_model is not None:
self.punc_kwargs.update(cfg) self.punc_kwargs.update(cfg)
punc_res = self.generate(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg) punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
result["text_with_punc"] = punc_res[0]["text"] result["text_with_punc"] = punc_res[0]["text"]
# speaker embedding cluster after resorted # speaker embedding cluster after resorted

21
funasr/bin/inference.py
View File

@ -1,25 +1,7 @@
import json
import time
import torch
import hydra import hydra
import random
import string
import logging import logging
import os.path
from tqdm import tqdm
from omegaconf import DictConfig, OmegaConf, ListConfig from omegaconf import DictConfig, OmegaConf, ListConfig
from funasr.register import tables
from funasr.utils.load_utils import load_bytes
from funasr.download.file import download_from_url
from funasr.download.download_from_hub import download_model
from funasr.utils.vad_utils import slice_padding_audio_samples
from funasr.train_utils.set_all_random_seed import set_all_random_seed
from funasr.train_utils.load_pretrained_model import load_pretrained_model
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
from funasr.utils.timestamp_tools import timestamp_sentence
from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
from funasr.models.campplus.cluster_backend import ClusterBackend
from funasr.auto.auto_model import AutoModel from funasr.auto.auto_model import AutoModel
@ -41,10 +23,9 @@ def main_hydra(cfg: DictConfig):
if kwargs.get("debug", False): if kwargs.get("debug", False):
import pdb; pdb.set_trace() import pdb; pdb.set_trace()
model = AutoModel(**kwargs) model = AutoModel(**kwargs)
res = model(input=kwargs["input"]) res = model.generate(input=kwargs["input"])
print(res) print(res)
if __name__ == '__main__': if __name__ == '__main__':
main_hydra() main_hydra()