Pre-trained model
This page describes how to download a pre-trained Cohere Transcribe model for sherpa-onnx.
Download the released archive from https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models:
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01.tar.bz2
tar xvf sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01.tar.bz2
rm sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01.tar.bz2
ls -lh sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01
You should see the following output:
-rw-r--r--@ 1 fangjun staff 146M 1 Apr 19:00 decoder.int8.onnx
-rw-r--r--@ 1 fangjun staff 2.9M 1 Apr 19:01 encoder.int8.onnx
-rw-r--r--@ 1 fangjun staff 2.5G 1 Apr 19:01 encoder.int8.onnx.data
-rw-r--r--@ 1 fangjun staff 294B 1 Apr 19:00 README.md
drwxr-xr-x@ 11 fangjun staff 352B 2 Apr 19:14 test_wavs
-rw-r--r--@ 1 fangjun staff 203K 2 Apr 14:16 tokens.txt
Decode a short audio file
The following example shows how to decode a wav file:
./build/bin/sherpa-onnx-offline \
--cohere-transcribe-encoder=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/encoder.int8.onnx \
--cohere-transcribe-decoder=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/decoder.int8.onnx \
--cohere-transcribe-language=en \
--cohere-transcribe-use-punct=1 \
--cohere-transcribe-use-itn=1 \
--tokens=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/tokens.txt \
--num-threads=2 \
./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/test_wavs/en.wav
Note
Cohere Transcribe requires the input language to be set explicitly. Please
replace en with the language of your audio, such as de or zh.
The output logs are given below:
/Users/fangjun/open-source/sherpa-onnx/sherpa-onnx/csrc/parse-options.cc:Read:373 ./build/bin/sherpa-onnx-offline --cohere-transcribe-encoder=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/encoder.int8.onnx --cohere-transcribe-decoder=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/decoder.int8.onnx --cohere-transcribe-language=en --cohere-transcribe-use-punct=1 --cohere-transcribe-use-itn=1 --num-threads=2 --tokens=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/tokens.txt ./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/test_wavs/en.wav
OfflineRecognizerConfig(feat_config=FeatureExtractorConfig(sampling_rate=16000, feature_dim=80, low_freq=20, high_freq=-400, dither=0, normalize_samples=True, snip_edges=False), model_config=OfflineModelConfig(transducer=OfflineTransducerModelConfig(encoder_filename="", decoder_filename="", joiner_filename=""), paraformer=OfflineParaformerModelConfig(model=""), nemo_ctc=OfflineNemoEncDecCtcModelConfig(model=""), whisper=OfflineWhisperModelConfig(encoder="", decoder="", language="", task="transcribe", tail_paddings=-1, enable_token_timestamps=False, enable_segment_timestamps=False), fire_red_asr=OfflineFireRedAsrModelConfig(encoder="", decoder=""), tdnn=OfflineTdnnModelConfig(model=""), zipformer_ctc=OfflineZipformerCtcModelConfig(model=""), wenet_ctc=OfflineWenetCtcModelConfig(model=""), sense_voice=OfflineSenseVoiceModelConfig(model="", language="auto", use_itn=False), moonshine=OfflineMoonshineModelConfig(preprocessor="", encoder="", uncached_decoder="", cached_decoder="", merged_decoder=""), dolphin=OfflineDolphinModelConfig(model=""), canary=OfflineCanaryModelConfig(encoder="", decoder="", src_lang="", tgt_lang="", use_pnc=True), cohere_transcribe=OfflineCohereTranscribeModelConfig(encoder="./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/encoder.int8.onnx", decoder="./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/decoder.int8.onnx", language="en", use_punct=True, use_itn=True), omnilingual=OfflineOmnilingualAsrCtcModelConfig(model=""), funasr_nano=OfflineFunASRNanoModelConfig(encoder_adaptor="", llm="", embedding="", tokenizer="", system_prompt="You are a helpful assistant.", user_prompt="语音转写：", max_new_tokens=512, temperature=1e-06, top_p=0.8, seed=42, language="", itn=True, hotwords=""), medasr=OfflineMedAsrCtcModelConfig(model=""), fire_red_asr_ctc=OfflineFireRedAsrCtcModelConfig(model=""), qwen3_asr=OfflineQwen3ASRModelConfig(conv_frontend="", encoder="", decoder="", tokenizer="", hotwords="", max_total_len=512, max_new_tokens=128, temperature=1e-06, top_p=0.8, seed=42), telespeech_ctc="", tokens="./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/tokens.txt", num_threads=2, debug=False, provider="cpu", model_type="", modeling_unit="cjkchar", bpe_vocab=""), lm_config=OfflineLMConfig(model="", scale=0.5, lodr_scale=0.01, lodr_fst="", lodr_backoff_id=-1), ctc_fst_decoder_config=OfflineCtcFstDecoderConfig(graph="", max_active=3000), decoding_method="greedy_search", max_active_paths=4, hotwords_file="", hotwords_score=1.5, blank_penalty=0, rule_fsts="", rule_fars="", hr=HomophoneReplacerConfig(lexicon="", rule_fsts=""))
Creating recognizer ...
recognizer created in 2.074 s
Started
Done!
./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/test_wavs/en.wav
----
num threads: 2
decoding method: greedy_search
Elapsed seconds: 0.778 s
Real time factor (RTF): 0.778 / 5.768 = 0.135
{"lang": "", "emotion": "", "event": "", "text": " Ask not what your country can do for you, ask what you can do for your country.", "timestamps": [], "durations": [], "tokens":[" As", "k", " not", " what", " your", " country", " can", " do", " for", " you", ",", " ask", " what", " you", " can", " do", " for", " your", " country", "."], "ys_log_probs": [], "words": []}
Decode a long audio file with VAD (Example 1/2, English)
The following examples show how to decode a very long audio file with the help of VAD.
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/Obama.wav
./build/bin/sherpa-onnx-vad-with-offline-asr \
--silero-vad-model=./silero_vad.onnx \
--silero-vad-threshold=0.2 \
--silero-vad-min-speech-duration=0.2 \
--cohere-transcribe-encoder=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/encoder.int8.onnx \
--cohere-transcribe-decoder=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/decoder.int8.onnx \
--tokens=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/tokens.txt \
--cohere-transcribe-language=en \
--cohere-transcribe-use-punct=1 \
--cohere-transcribe-use-itn=1 \
--num-threads=2 \
./Obama.wav
| Wave filename | Content |
|---|---|
| Obama.wav |
You should see the following output:
/Users/fangjun/open-source/sherpa-onnx/sherpa-onnx/csrc/parse-options.cc:Read:373 ./build/bin/sherpa-onnx-vad-with-offline-asr --silero-vad-model=./silero_vad.onnx --silero-vad-threshold=0.2 --silero-vad-min-speech-duration=0.2 --cohere-transcribe-encoder=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/encoder.int8.onnx --cohere-transcribe-decoder=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/decoder.int8.onnx --tokens=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/tokens.txt --cohere-transcribe-language=en --cohere-transcribe-use-punct=1 --cohere-transcribe-use-itn=1 --num-threads=2 ./Obama.wav
VadModelConfig(silero_vad=SileroVadModelConfig(model="./silero_vad.onnx", threshold=0.2, min_silence_duration=0.5, min_speech_duration=0.2, max_speech_duration=20, window_size=512, neg_threshold=-1), ten_vad=TenVadModelConfig(model="", threshold=0.5, min_silence_duration=0.5, min_speech_duration=0.25, max_speech_duration=20, window_size=256), sample_rate=16000, num_threads=1, provider="cpu", debug=False)
OfflineRecognizerConfig(feat_config=FeatureExtractorConfig(sampling_rate=16000, feature_dim=80, low_freq=20, high_freq=-400, dither=0, normalize_samples=True, snip_edges=False), model_config=OfflineModelConfig(transducer=OfflineTransducerModelConfig(encoder_filename="", decoder_filename="", joiner_filename=""), paraformer=OfflineParaformerModelConfig(model=""), nemo_ctc=OfflineNemoEncDecCtcModelConfig(model=""), whisper=OfflineWhisperModelConfig(encoder="", decoder="", language="", task="transcribe", tail_paddings=-1, enable_token_timestamps=False, enable_segment_timestamps=False), fire_red_asr=OfflineFireRedAsrModelConfig(encoder="", decoder=""), tdnn=OfflineTdnnModelConfig(model=""), zipformer_ctc=OfflineZipformerCtcModelConfig(model=""), wenet_ctc=OfflineWenetCtcModelConfig(model=""), sense_voice=OfflineSenseVoiceModelConfig(model="", language="auto", use_itn=False), moonshine=OfflineMoonshineModelConfig(preprocessor="", encoder="", uncached_decoder="", cached_decoder="", merged_decoder=""), dolphin=OfflineDolphinModelConfig(model=""), canary=OfflineCanaryModelConfig(encoder="", decoder="", src_lang="", tgt_lang="", use_pnc=True), cohere_transcribe=OfflineCohereTranscribeModelConfig(encoder="./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/encoder.int8.onnx", decoder="./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/decoder.int8.onnx", language="en", use_punct=True, use_itn=True), omnilingual=OfflineOmnilingualAsrCtcModelConfig(model=""), funasr_nano=OfflineFunASRNanoModelConfig(encoder_adaptor="", llm="", embedding="", tokenizer="", system_prompt="You are a helpful assistant.", user_prompt="语音转写：", max_new_tokens=512, temperature=1e-06, top_p=0.8, seed=42, language="", itn=True, hotwords=""), medasr=OfflineMedAsrCtcModelConfig(model=""), fire_red_asr_ctc=OfflineFireRedAsrCtcModelConfig(model=""), qwen3_asr=OfflineQwen3ASRModelConfig(conv_frontend="", encoder="", decoder="", tokenizer="", hotwords="", max_total_len=512, max_new_tokens=128, temperature=1e-06, top_p=0.8, seed=42), telespeech_ctc="", tokens="./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/tokens.txt", num_threads=2, debug=False, provider="cpu", model_type="", modeling_unit="cjkchar", bpe_vocab=""), lm_config=OfflineLMConfig(model="", scale=0.5, lodr_scale=0.01, lodr_fst="", lodr_backoff_id=-1), ctc_fst_decoder_config=OfflineCtcFstDecoderConfig(graph="", max_active=3000), decoding_method="greedy_search", max_active_paths=4, hotwords_file="", hotwords_score=1.5, blank_penalty=0, rule_fsts="", rule_fars="", hr=HomophoneReplacerConfig(lexicon="", rule_fsts=""))
Creating recognizer ...
Recognizer created!
Started
Reading: ./Obama.wav
Started!
7.248 -- 8.204: Thank you.
8.976 -- 12.140: Thank you, everybody. All right, everybody go ahead and have a seat.
13.104 -- 14.540: How's everybody doing today?
18.704 -- 22.892: How about Tim Spicer?
25.936 -- 31.884: I am here with students at Wakefield High School in Arlington, Virginia.
32.720 -- 48.844: And we've got students tuning in from all across America, from kindergarten through 12th grade. And I am just so glad that all could join us today. And I want to thank Wakefield for being such an outstanding host. Give yourselves a big round of applause.
54.416 -- 55.436: Now I know that
56.240 -- 58.892: For many of you, today is the first day of school.
59.600 -- 69.452: And for those of you in kindergarten or starting middle or high school, it's your first day in a new school, so it's understandable if you're a little nervous.
70.640 -- 76.332: I imagine there's some seniors out there who are feeling pretty good right now. With just one more year to go.
78.800 -- 87.180: And no matter what grade you're in, some of you are probably wishing it were still summer and you could have stayed in bed just a little bit longer this morning.
87.984 -- 89.100: I know that feeling.
91.664 -- 111.708: When I was young, my family lived overseas. I lived in Indonesia for a few years. And my mother, she didn't have the money to send me where all the American kids went to school. But she thought it was important for me to keep up with American education. So she decided to teach me extra lessons herself.
112.240 -- 118.700: Monday through Friday, but because she had to go to work, the only time she could do it was at 4 30 in the morning.
120.048 -- 127.244: Now, as you might imagine, I wasn't too happy about getting up that early. A lot of times I'd fall asleep right there at the kitchen table.
128.272 -- 135.340: But whenever I complained, my mother would just give me one of those looks, and she'd say, This is no picnic for me either, Buster.
137.104 -- 145.132: So, I know that some of you are still adjusting to being back at school, but I'm here today because I have something important to discuss with you.
145.808 -- 153.740: I'm here because I want to talk with you about your education and what's expected of all of you in this new school year.
154.448 -- 160.268: I've given a lot of speeches about education, and I've talked about responsibility a lot.
160.816 -- 178.220: I've talked about teachers' responsibility for inspiring students and pushing you to learn. I've talked about your parents' responsibility for making sure you stay on track and you get your homework done and don't spend every waking hour in front of the TV or with the Xbox.
179.088 -- 180.716: I've talked a lot about
181.360 -- 193.452: Your government's responsibility for setting high standards and supporting teachers and principals and turning around schools that aren't working, where students aren't getting the opportunities that they deserve.
194.000 -- 195.276: But at the end of the day
196.016 -- 206.156: We can have the most dedicated teachers, the most supportive parents, the best schools in the world, and none of it will make a difference. None of it will matter.
206.704 -- 210.604: unless all of you fulfill your responsibilities.
211.248 -- 223.404: Unless you show up to those schools, unless you pay attention to those teachers, unless you listen to your parents and grandparents and other adults and put in the hard work it takes to succeed.
224.656 -- 230.924: And that's what I want to focus on today the responsibility each of you has for your education.
231.728 -- 234.796: I want to start with the responsibility you have to yourself.
235.696 -- 238.988: Every single one of you has something that you're good at.
239.760 -- 242.412: Every single one of you has something to offer.
242.992 -- 247.404: And you have a responsibility to yourself to discover what that is.
248.336 -- 251.564: That's the opportunity an education can provide.
252.336 -- 265.900: Maybe you could be a great writer, maybe even good enough to write a book or articles in a newspaper, but you might not know it until you write that English paper, that English class paper that's assigned to you.
266.704 -- 278.668: Maybe you could be an innovator or an inventor, maybe even good enough to come up with the next iPhone or the new medicine or vaccine. But you might not know it until you do your project for your science class.
279.824 -- 289.964: Maybe you could be a mayor, or a senator, or a Supreme Court justice. But you might not know that until you join student government or the debate team.
291.568 -- 309.516: And no matter what you want to do with your life, I guarantee that you'll need an education to do it. You want to be a doctor or a teacher or a police officer. You want to be a nurse or an architect, a lawyer or a member of our military. You're going to need a good education for every single one of those careers.
310.064 -- 314.348: You cannot drop out of school and just drop into a good job.
315.184 -- 319.852: You've got to train for it and work for it and learn for it.
320.528 -- 323.628: And this isn't just important for your own life and your own future.
324.688 -- 332.812: What you make of your education will decide nothing less than the future of this country. The future of America depends on you.
num threads: 2
decoding method: greedy_search
Elapsed seconds: 34.787 s
Real time factor (RTF): 34.787 / 334.234 = 0.104
Decode a long audio file with VAD (Example 2/2, Chinese)
The following examples show how to decode a very long audio file with the help of VAD.
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/lei-jun-test.wav
./build/bin/sherpa-onnx-vad-with-offline-asr \
--silero-vad-model=./silero_vad.onnx \
--silero-vad-threshold=0.2 \
--silero-vad-min-speech-duration=0.2 \
--cohere-transcribe-encoder=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/encoder.int8.onnx \
--cohere-transcribe-decoder=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/decoder.int8.onnx \
--tokens=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/tokens.txt \
--cohere-transcribe-language=zh \
--cohere-transcribe-use-punct=1 \
--cohere-transcribe-use-itn=1 \
--num-threads=2 \
./lei-jun-test.wav
| Wave filename | Content |
|---|---|
| lei-jun-test.wav |
You should see the following output:
/Users/fangjun/open-source/sherpa-onnx/sherpa-onnx/csrc/parse-options.cc:Read:373 ./build/bin/sherpa-onnx-vad-with-offline-asr --silero-vad-model=./silero_vad.onnx --silero-vad-threshold=0.2 --silero-vad-min-speech-duration=0.2 --cohere-transcribe-encoder=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/encoder.int8.onnx --cohere-transcribe-decoder=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/decoder.int8.onnx --tokens=./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/tokens.txt --cohere-transcribe-language=zh --cohere-transcribe-use-punct=1 --cohere-transcribe-use-itn=1 --num-threads=2 ./lei-jun-test.wav
VadModelConfig(silero_vad=SileroVadModelConfig(model="./silero_vad.onnx", threshold=0.2, min_silence_duration=0.5, min_speech_duration=0.2, max_speech_duration=20, window_size=512, neg_threshold=-1), ten_vad=TenVadModelConfig(model="", threshold=0.5, min_silence_duration=0.5, min_speech_duration=0.25, max_speech_duration=20, window_size=256), sample_rate=16000, num_threads=1, provider="cpu", debug=False)
OfflineRecognizerConfig(feat_config=FeatureExtractorConfig(sampling_rate=16000, feature_dim=80, low_freq=20, high_freq=-400, dither=0, normalize_samples=True, snip_edges=False), model_config=OfflineModelConfig(transducer=OfflineTransducerModelConfig(encoder_filename="", decoder_filename="", joiner_filename=""), paraformer=OfflineParaformerModelConfig(model=""), nemo_ctc=OfflineNemoEncDecCtcModelConfig(model=""), whisper=OfflineWhisperModelConfig(encoder="", decoder="", language="", task="transcribe", tail_paddings=-1, enable_token_timestamps=False, enable_segment_timestamps=False), fire_red_asr=OfflineFireRedAsrModelConfig(encoder="", decoder=""), tdnn=OfflineTdnnModelConfig(model=""), zipformer_ctc=OfflineZipformerCtcModelConfig(model=""), wenet_ctc=OfflineWenetCtcModelConfig(model=""), sense_voice=OfflineSenseVoiceModelConfig(model="", language="auto", use_itn=False), moonshine=OfflineMoonshineModelConfig(preprocessor="", encoder="", uncached_decoder="", cached_decoder="", merged_decoder=""), dolphin=OfflineDolphinModelConfig(model=""), canary=OfflineCanaryModelConfig(encoder="", decoder="", src_lang="", tgt_lang="", use_pnc=True), cohere_transcribe=OfflineCohereTranscribeModelConfig(encoder="./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/encoder.int8.onnx", decoder="./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/decoder.int8.onnx", language="zh", use_punct=True, use_itn=True), omnilingual=OfflineOmnilingualAsrCtcModelConfig(model=""), funasr_nano=OfflineFunASRNanoModelConfig(encoder_adaptor="", llm="", embedding="", tokenizer="", system_prompt="You are a helpful assistant.", user_prompt="语音转写：", max_new_tokens=512, temperature=1e-06, top_p=0.8, seed=42, language="", itn=True, hotwords=""), medasr=OfflineMedAsrCtcModelConfig(model=""), fire_red_asr_ctc=OfflineFireRedAsrCtcModelConfig(model=""), qwen3_asr=OfflineQwen3ASRModelConfig(conv_frontend="", encoder="", decoder="", tokenizer="", hotwords="", max_total_len=512, max_new_tokens=128, temperature=1e-06, top_p=0.8, seed=42), telespeech_ctc="", tokens="./sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01/tokens.txt", num_threads=2, debug=False, provider="cpu", model_type="", modeling_unit="cjkchar", bpe_vocab=""), lm_config=OfflineLMConfig(model="", scale=0.5, lodr_scale=0.01, lodr_fst="", lodr_backoff_id=-1), ctc_fst_decoder_config=OfflineCtcFstDecoderConfig(graph="", max_active=3000), decoding_method="greedy_search", max_active_paths=4, hotwords_file="", hotwords_score=1.5, blank_penalty=0, rule_fsts="", rule_fars="", hr=HomophoneReplacerConfig(lexicon="", rule_fsts=""))
Creating recognizer ...
Recognizer created!
Started
Reading: ./lei-jun-test.wav
Started!
29.776 -- 35.788: 晚上好欢迎大家来参加今天晚上的活动谢谢大家
42.160 -- 45.996: 这是我第四次年度演讲
47.024 -- 49.868: 前三次呢因为情的原因
50.512 -- 55.340: 都在小米科技内办现场的人很少
56.176 -- 57.388: 这是第四次
58.192 -- 66.892: 我们想了想我们孩子想一个比较大的会然后呢让我们的新朋友老朋友一起一
67.760 -- 70.828: 今天的话呢我们就在北京的
71.664 -- 74.828: 国家会议中心呢办了这么一个活动
75.472 -- 85.868: 现场呢来了很多人大概有三千五百人还有很多很多的朋友呢通过观看直播的方式来参与
86.352 -- 91.308: 再一次呢对大家的参加表示感谢谢大家
98.512 -- 99.692: 两个月前。
100.400 -- 104.396: 我参加了今年武大学的业礼
105.936 -- 107.276: 今年的是。
107.888 -- 110.572: 武大学建校一百三十周年。
111.760 -- 117.196: 作为校友被母校请在业礼上自
118.032 -- 122.732: 这对我来说是至高无上的。
123.664 -- 128.556: 站在讲台的那一刻面对全校师生
129.200 -- 134.252: 关于武大的所有的记一下子现在海里
134.960 -- 139.436: 今天呢我就先和大家聊聊五大往事
141.840 -- 143.980: 那还是三十六年前。
145.936 -- 147.660: 一九八七年。
148.688 -- 151.564: 我呢考上了武大学的计算机系。
152.688 -- 156.748: 在武大学的图书里看了一本书
157.584 -- 161.804: 谷火。建立了我一生的想。
163.312 -- 164.652: 看完书以后
165.264 -- 166.636: 热血
167.600 -- 169.548: 激动的睡不着觉。
170.416 -- 171.404: 我还记得。
172.016 -- 174.700: 那天晚上星光很。
175.408 -- 179.820: 我就在五大的操场上就是幕上这个操场
180.816 -- 185.228: 走了一又一走了整整一个晚上
186.480 -- 187.916: 我心里有火
188.912 -- 192.076: 我也想办一个大的公司
193.968 -- 195.020: 就是这样。
197.648 -- 202.316: 想之火在我心里底点燃了
209.968 -- 212.396: 是一个大一的心声
220.496 -- 222.636: 是一个大一的心声
223.984 -- 226.892: 一个从城里出来的年轻人
228.368 -- 230.604: 什么也不会什么也没有
231.568 -- 236.204: 就想一家大的公司,这不就是天夜
237.616 -- 239.788: 这么离的一个想。
240.400 -- 242.316: 该如何实现呢
243.856 -- 246.924: 那天晚上我想了一整晚上。
247.952 -- 249.068: 说实话。
250.352 -- 253.868: 越想越,完全不清头
254.960 -- 265.836: 后来我在想,哎,干别想了,把书念好是正式。所以呢,我就下定决心,跟跟真正读书。
266.640 -- 267.468: 那么。
268.496 -- 271.564: 我怎么能够把书读的不同反响呢?
num threads: 2
decoding method: greedy_search
Elapsed seconds: 22.658 s
Real time factor (RTF): 22.658 / 272.448 = 0.083