Whisper by OpenAI

For 语音recognize 的神经网络，An open-source game-changer in speech recognition

Voice and conferencing speech synthesis

https://openai.com/research/whisper

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OpenAI Whisper is an open source automatic speech recognition (ASR) model released in September 2022. It is trained on 680,000 hours of multilingual audio data and supports speech-to-text and speech translation in 99 languages. It was developed by the OpenAI research team and is open source under the MIT license, allowing anyone to download it for free, deploy it locally, or integrate it into their own products. Whisper's core value lies in unifying otherwise fragmented speech processing tasks—recognition, translation, language detection, timestamp alignment—into a single end-to-end model. Unlike previous speech recognition solutions that required repeated tuning for specific scenarios, Whisper is extremely robust and has good adaptability to accents, background noise, and professional terminology, and can be used out of the box.
Whisper’s breakthrough comes from the extremely large-scale training data set built by OpenAI. The research team crawled 680,000 hours of audio from the Internet, covering 99 languages, and conducted weakly supervised training with corresponding text annotations. "Weak supervision" means that the training data is not carefully annotated by professional annotators, but comes from naturally distributed Internet content, which greatly expands the size of the data and allows the model to learn to deal with various noises and changes in real scenes. The model is based on Transformer's Encoder-Decoder (seq2seq) architecture. The input is a Mel spectrogram and the output is a text token sequence. Through special control tokens, the same set of model weights can be switched between different tasks such as transcription, translation, and language detection.
Whisper has a total of 9 model variants, covering different hardware environments and accuracy requirements: The smallest tiny model has only 39M parameters, takes up about 75MB of disk space, requires only 1GB of video memory, is 10 times faster than the largest model, and can run on low-power devices such as Raspberry Pi. The English word error rate is about 7.6%, making it suitable for real-time prototype verification and low-resource devices. The base model (74M parameters) improves the English accuracy to about 5% based on tiny, and consumes almost the same resources. It is a common choice for entry-level applications. The small model (244M parameters) is the recommended starting point for most individual users and developers, and runs smoothly on modern laptops with English word error rates down to about 3.4%. The medium model (769M parameters) is suitable for professional transcription scenarios, requires about 5GB of video memory, has significantly enhanced multi-language processing capabilities, and has a word error rate of about 5%. large-v3 (1.55B parameters) is the flagship version, with an English word error rate of about 2.4%, multi-language word error rate of about 3.5%, and requires about 10GB of video memory. Suitable for professional scenarios that require extremely high accuracy, such as medical records and legal document transcription. Turbo (809M parameters) is a new member released in September 2024. It is refined from large-v3 through knowledge distillation technology. Its accuracy is close to the large level (English WER is about 2.5%), but its speed is 8 times that of large, and it is extremely cost-effective. The only limitation is that it does not support speech translation tasks and can only do transcription.
In the field of content creation, Whisper has become a standard tool for podcasters and vloggers. Converting a one-hour podcast audio to text, which used to take professionals several hours, can now be completed in less than 10 minutes on an ordinary laptop with the help of the Whisper turbo model, and supports direct export in .srt and .vtt subtitle formats. In corporate office scenarios, automatic transcription of meeting recordings greatly reduces the burden of written records. Combined with speaker separation tools (such as WhisperX), different speakers can also be distinguished and structured meeting minutes can be generated. In terms of accessibility, Whisper is widely used to provide real-time subtitles for the hearing-impaired. It captures video playback audio through the system's internal microphone and automatically generates subtitles to overlay on the screen. Local deployment is Whisper’s core advantage over commercial cloud service APIs. Law firms, hospitals, financial institutions and other industries that are sensitive to data privacy can deploy Whisper on local servers to achieve voice transcription with zero data leakage. Runs smoothly on CPU without GPU thanks to whisper.cpp (C++ implementation) or faster-whisper (CTranslate2 backend).
The easiest way to use it is to install the official Python package: pip install openai-whisper whisper recording.mp3 --model turbo --language zh Installation and transcription can be completed with three lines of commands. For users who don’t want to deploy locally, OpenAI provides the Whisper API, priced at about $0.006 per minute. Upload audio files to get transcription results without requiring GPU resources. Developers can integrate it into any application through the Python SDK: import whisper model = whisper.load_model("turbo") result = model.transcribe("audio.mp3") print(result["text"])
In October 2024, media such as Fortune and TechCrunch reported on Whisper’s hallucination problem—the model sometimes made up words and phrases in the transcription that did not exist in the original audio. This problem is most obvious in silent segments, background noise, and low-quality audio. One developer discovered that nearly every one of the 26,000 transcripts processed contained hallucinatory content. The problem of hallucinations is particularly dangerous in medical settings: if a doctor dictates a medical record with a non-existent drug name or diagnostic description, the consequences can be disastrous. OpenAI responded that it is continuing to improve, but has not yet given a specific timetable for fixes. Current mitigation options include using Voice Activity Detection (VAD) to remove non-speech segments before inputting into Whisper, as well as manual review of the transcription results, especially in high-risk scenarios.
In the three years since Whisper has been open sourced, an extremely active third-party ecosystem has grown around it. whisper.cpp has accumulated 47k+ stars on GitHub and is currently the best solution for local deployment on Apple Silicon Mac. faster-whisper uses quantization and the CTranslate2 backend to increase speed by 4-10 times with the same accuracy and reduce video memory requirements by 50%. With enhanced word-level timestamping and speaker separation capabilities, WhisperX is a popular choice for subtitling scenarios. There's even Whisper Web - running completely in the browser via WebGPU, no servers required, completely protecting privacy.
Whisper is one of the most influential open source AI tools in recent years. It lowers the technical threshold for speech-to-text, making professional-level speech recognition capabilities that were originally owned by large enterprises within reach. For content creators, developers, and researchers, it is currently the most cost-effective ASR solution. It should be noted that the hallucination problem makes it unsuitable for direct use in high-risk fields (medical, legal) without manual review; the real-time performance is also not as good as commercial streaming recognition services. In these special scenarios, careful evaluation is required based on specific needs. Overall, OpenAI Whisper has become an infrastructure-level tool that cannot be ignored in the field of speech recognition due to its three major advantages: open source and free, strong multi-language support and local deployment.

User Reviews

Bruce.Diaz_2020

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Take Whisper and transcribe an entire year's archive of podcasts, hundreds of hours of audio. Although it took more than a week to run, I am very satisfied with the final result. Each podcast has a searchable text version.

ElzeárioMonteiro

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Vibe, the Whisper GUI tool, has been a real life saver since it came out. Finally, I no longer have to deal with the command line. Download it and use it, just drag and drop the audio to produce text, which is great for people around you.

Alexander.Murray_Plus

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Whisper changed my daily workflow completely. I spend most of my day voice typing now. The accuracy is a massive leap over older dictation tools.

RRussell_77

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Whisper is a game changer for podcast transcription. I process 3 hours of audio daily on my M1 Mac and it handles everything flawlessly. Cant beat free and local.

8kosok

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Whisper's Chinese recognition is really good. I tried it with an audio piece with a Sichuan accent. The medium model can achieve an accuracy of more than 90%, which is unexpected.

DBell_Pro

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Whisper itself is powerful, but too unfriendly to ordinary users. My girlfriend wanted to use it to transcribe class recordings, but she gave up when she saw that she had to install Python and the command line. I hope the official will release a fool-proof client.

杨心怡

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The hallucination issue is real though. I had it make up an entire paragraph about a patient's medication history in a medical transcript. Always double check sensitive content.

枫叶398

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After using Whisper for a few months, the biggest feeling is that it is free and offline. The company’s recordings that require confidentiality no longer need to be uploaded to a third party, and data security is very important.

Richard.Castillo_2022

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I used Whisper large-v3 to run a line from a Japanese animation, and the Japanese recognition accuracy was actually quite high. Even fast-speaking dialogue could be correctly transcribed. It’s amazing that the open source model has reached this level.

Jordan.Powell7

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Whisper also performs well when processing low-bitrate audio. I have an 8kbps phone call recording, and large-v3 can actually transcribe it into 7788, which is much better than I expected.

Thomas_Wilson

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Locally deploying Whisper large-v3, an RTX 3060 runs just fine, but the video memory is tight. It takes about six minutes to process one hour of audio, which is acceptable.

BMoralesX666

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I switched from Google Speech-to-Text to Whisper for my startup. Cost went from ~$300/month to essentially zero. The accuracy is comparable for clean audio.

RGonzalez_66

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There were a lot of pitfalls when deploying Whisper, mainly due to incompatible ffmpeg versions and Python dependency conflicts. It is recommended to use conda directly to build a virtual environment, which saves a lot of worry.

NFTCollector54

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After using Whisper, I can’t go back. I used to use iFlytek all the time, and the free time every month was never enough. Whisper is unlimited and offline, which is critical for heavy users.

BMurphy_778

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Whisper's multilingual support is unmatched. We handle 8 languages for our customer service transcripts and it handles accents remarkably well. Big fan.

DeFiMgx

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Compared with iFlytek, Whisper's Chinese accuracy is indeed a little worse, but it is better because it is free and has good multi-language support. When I do cross-border business, I often have to deal with Chinese, English, Japanese and Korean, and Whisper is a great tool.

MelissaKingSr

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Whisper is a bit blind when encountering scenarios where multiple people are talking at the same time, and needs to be used with a speaker separation model. But having said that, the performance of a single voice channel is really impressive.

Robert.Flores_7754

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What surprises me most about Whisper is how well it handles technical vocabulary. I dictate code documentation and API specs all day and it gets them right 95% of the time.

Teresa.Lewis_99945

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I use it to batch transcribe podcast subtitles. Whisper can handle dozens of audio files every day. With Python script automation, a set of processes can be run without manual intervention.

CYhar

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Running Whisper large-v3 on CPU is painful. 1 hour of audio takes about 30 minutes even with whisper.cpp. But for batch processing it's absolutely worth the wait.

丁贞

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Whisper occasionally overturns in professional terminology, and the recognition rate of medical terminology and legal provisions is not very good, but it is completely sufficient for daily conversations and ordinary meeting notes.

sfvr1qm

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I used Whisper to transcribe class recordings, and the medium model takes about seven or eight minutes to run a 45-minute class. Export SRT subtitles and use it with note-taking software to double your review efficiency.

iJorisSchmitt_dev

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Whisper + pyannote for diarization is the killer combo for meeting transcription. Not as polished as Otter but way more control over your data.

Jason.RuizJr2

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I tried the quantized version of faster-whisper, and the inference speed was nearly twice as fast, with almost no loss of accuracy. It is highly recommended for users with large-scale transcription needs to try it.

BRuizSr

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The Whisper API is deployed on Alibaba Cloud Function Compute and pays according to the call volume, which is extremely low cost. Five hundred thousand minutes of audio processing cost less than 2,000 yuan, which is much more cost-effective than buying commercial services.

高雅

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I built a small desktop app wrapping Whisper for my non-tech colleagues. They love it. The underlying model is amazing — it just needs better UI to reach mainstream users.

trueMoniqueHanke_dev

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Whisper's hallucination problem does exist, but adding VAD preprocessing can basically solve it. It’s better than those who upload recordings to the cloud and don’t tell you how to process the data.

wef3bkdk

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Whisper's recognition of classical Chinese and ancient poems was also surprising. I tried the recitation audios of "Three Character Classic" and "Preface to Prince Teng's Pavilion", and the large model is basically word-for-word. This training data is so rich.

HariniSaldanha

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Tested Whisper against Deepgram Nova-3 on noisy field recordings. Whisper held its own surprisingly well. The gap has narrowed a lot since 2024.

Terry.Fisher007014

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Switching from Dragon NaturallySpeaking to Whisper, although the deployment took some effort, the current voice transcription effect is better than that of older products, and it is free.

Whisper by OpenAI

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