Reka Speech
Reka Speech is an 850-million-parameter speech model from Reka AI, designed for high-throughput multilingual transcription and speech-to-speech translation.
Publisher review
Reka Speech is an 850-million-parameter speech model from Reka AI, designed for high-throughput multilingual transcription and speech-to-speech translation. It is built for developers and enterprises processing large volumes of offline audio — such as call center logs, meeting archives, or media libraries — where speed and accuracy matter more than real-time interactivity. The architecture combines a 300-million-parameter audio encoder with a 550-million-parameter Transformer decoder, allowing it to handle ten languages: English, Japanese, Korean, Chinese, French, Spanish, German, Italian, Portuguese, and Arabic.
On modern GPUs like the H100, Reka Speech runs 8x to 35x faster than alternative methods, making it a strong candidate for batch jobs that previously required hours of processing. The model also generates timestamps for each transcribed segment and can translate bidirectionally between English and any supported language, which eliminates the need for separate translation pipelines. Inference is accelerated through a modified version of vLLM, an open-source inference engine typically used for large language models, adapted here for speech decoding.
This optimization is key to achieving the reported throughput gains without sacrificing output quality. In benchmark comparisons on the Common Voice 16.1 dataset, Reka Speech outperforms OpenAI Whisper Large v3 on transcription accuracy, a notable result given Whisper's dominance in the open-source speech recognition space. Its closest competitors include WhisperX, Faster-whisper, and the original OpenAI Whisper, all of which are widely used for similar tasks but generally lack Reka Speech's translation capabilities and optimized inference pipeline.
However, Reka Speech is not designed for real-time or streaming use cases — it is explicitly positioned for offline batch processing. The model is available through Reka AI's RESTful API, with Python and JavaScript SDKs, and can be deployed in cloud, VPC, or fully air-gapped on-premise environments for enterprises with strict data residency requirements. While the company has not published standalone pricing for the speech API, Reka Flash (a related multimodal model) is priced at $0.26 per million tokens blended, suggesting a competitive per-unit cost for speech processing as well. The main trade-offs are the lack of real-time streaming support, no explicit mention of speaker diarization, a limited language set compared to some competitors, and the absence of a free tier or self-hosted open-source version of the speech model itself.
How it works
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850M-parameter architecture
Combines a 300M audio encoder with a 550M Transformer decoder for efficient multilingual speech processing.
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Multilingual transcription and translation
Supports ten languages and can translate bidirectionally between English and any supported language.
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8x-35x faster inference
On H100 GPUs, achieves 8x to 35x speedup over alternative methods using a modified vLLM engine.
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Timestamp generation
Produces timestamps for each transcribed segment, enabling alignment with source audio for downstream tasks.
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High-volume offline processing
Optimized for batch jobs like call center logs or media archives, not for real-time streaming.
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Competitive accuracy
Outperforms Whisper Large v3 on Common Voice 16.1 transcription benchmarks.
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Enterprise deployment options
Available via RESTful API, VPC, or fully air-gapped on-premise for data-sensitive environments.
Strengths and trade-offs
Strengths
- Runs 8x to 35x faster than alternative methods on modern GPUs like the H100.
- Outperforms OpenAI Whisper Large v3 on Common Voice 16.1 transcription accuracy.
- Supports bidirectional translation between English and nine other languages within a single model.
- Generates timestamps for transcriptions, enabling precise audio-text alignment for downstream processing.
Trade-offs
- Not designed for real-time or streaming use cases, limiting its applicability for live captioning or voice assistants.
- Language support is limited to ten languages, fewer than some competitors like Whisper which cover nearly 100 languages.
- No explicit support for speaker diarization, making it less suitable for multi-speaker transcription tasks out of the box.
- Pricing for the speech API is not publicly disclosed, making cost comparison difficult for budget-conscious teams.
Pricing context
Not explicitly mentioned in the sources; Reka Flash (a related multimodal model) is priced at $0.26 per million tokens blended (7:2:1 cache-input-output ratio).
Getting started with Reka Speech
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Sign up for API access
Navigate to the Reka AI website and create an account. After registration, request API credentials for the Reka Speech model. You will receive an API key and endpoint URL to authenticate your requests.
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Install the Python SDK
Open your terminal and run `pip install reka` to install the official Python SDK. This library provides convenient methods for sending audio files and handling responses from the Reka Speech API.
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Configure the API client
In your Python script, import the Reka client and set your API key using `reka.api_key = 'your-key-here'`. Optionally, specify the endpoint URL if using a custom deployment like VPC or on-premise.
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Transcribe an audio file
Call `reka.transcribe(file_path='your_audio.mp3', language='en')` to transcribe the audio. The response includes the transcript text and timestamps for each segment. For translation, add `translate_to='es'` to get English-to-Spanish output.
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Process batch audio files
Write a loop to iterate over a directory of audio files, calling the transcribe function for each. Store results in a list or write to a JSON file. This enables efficient offline processing of large volumes like call center logs or meeting archives.
Frequently Asked Questions
What is Reka Speech and what is it used for?
Reka Speech is an 850-million-parameter speech model from Reka AI for high-throughput multilingual transcription and speech-to-speech translation. It is designed for offline batch processing of large audio volumes like call center logs or meeting archives, not real-time streaming.
How fast is Reka Speech compared to alternatives?
On modern GPUs like the H100, Reka Speech runs 8x to 35x faster than alternative methods. This speedup is achieved through a modified vLLM inference engine adapted for speech decoding, making it ideal for batch jobs that previously required hours of processing.
Which languages does Reka Speech support?
Reka Speech supports ten languages: English, Japanese, Korean, Chinese, French, Spanish, German, Italian, Portuguese, and Arabic. It can transcribe and translate bidirectionally between English and any supported language within a single model, eliminating separate translation pipelines.
How does Reka Speech compare to OpenAI Whisper Large v3?
On the Common Voice 16.1 dataset, Reka Speech outperforms OpenAI Whisper Large v3 on transcription accuracy. It also offers built-in translation and faster inference, but supports fewer languages (10 vs. nearly 100) and lacks real-time streaming capabilities.
Can Reka Speech be deployed on-premise for data security?
Yes, Reka Speech is available through a RESTful API with Python and JavaScript SDKs. It can be deployed in cloud, VPC, or fully air-gapped on-premise environments, catering to enterprises with strict data residency requirements.
What are the main limitations of Reka Speech?
Reka Speech is not designed for real-time or streaming use cases like live captioning. It lacks explicit speaker diarization support, covers only ten languages, and its speech API pricing is not publicly disclosed, making cost comparison difficult.
Alternatives
- WhisperX ↗
- AssemblyAI ↗
- Deepgram ↗
How Reka Speech compares
Direct head-to-head against 3 competitors. Picked by 7wData.
Reka Speech
- Pricing
- Not explicitly mentioned in the sources; Reka Flash (a related multimodal model) is priced at $0.26 per million tokens blended (7:2:1 cache-input-output ratio).
- Target
- Reka Speech is an 850-million-parameter speech model from Reka AI, designed for high-throughput multilingual transcription and speech-to-speech translation.
- Strength
- Runs 8x to 35x faster than alternative methods on modern GPUs like the H100.
- Watch for
- Not designed for real-time or streaming use cases, limiting its applicability for live captioning or voice assistants.
WhisperX
- Pricing
- Open source (free)
- Target
- Developers needing timestamp alignment
- Deployment
- Python library
- Strength
- Word-level timestamp accuracy
- Watch for
- Not optimized for high-throughput batch processing
AssemblyAI
- Pricing
- $0.0001/sec (real-time), $0.000066/sec (async)
- Target
- Enterprises requiring API-based transcription
- Deployment
- Cloud API
- Strength
- Real-time streaming with punctuation
- Watch for
- Cost escalates with high-volume usage
Deepgram
- Pricing
- $0.0049/min (base model)
- Target
- Developers building voice applications
- Deployment
- Cloud or on-prem
- Strength
- Low-latency streaming API
- Watch for
- Custom model training requires enterprise plan
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Sources
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