Speech-to-Text Benchmark

Made in Vancouver, Canada by Picovoice

This repo is a minimalist and extensible framework for benchmarking different speech-to-text engines.

Table of Contents

• Data
• Metrics
• Engines
• Usage
• Results

Data

• LibriSpeech
• TED-LIUM
• Common Voice
• Multilingual LibriSpeech
• VoxPopuli
• Fleurs (Download instructions)

Metrics

Word Error Rate

Word error rate (WER) is the ratio of edit distance between words in a reference transcript and the words in the output of the speech-to-text engine to the number of words in the reference transcript.

Punctuation Error Rate

Punctuation Error Rate (PER) is the ratio of punctuation-specific errors between a reference transcript and the output of a speech-to-text engine to the number of punctuation-related operations in the reference transcript (more details in Section 3 of Meister et al.). We report PER results for periods (.) and question marks (?).

Core-Hour

The Core-Hour metric is used to evaluate the computational efficiency of the speech-to-text engine, indicating the number of CPU hours required to process one hour of audio. A speech-to-text engine with lower Core-Hour is more computationally efficient. We omit this metric for cloud-based engines.

Word Emission Latency

Word emission latency is used to evaluate the responsiveness of streaming speech-to-text engines. It measures the average delay from the point a word has finished being spoken to when its transcription is emitted by the engine. We measure this metric only for streaming engines.

Model Size

The aggregate size of models (acoustic and language), in MB. We omit this metric for cloud-based engines.

Engines

• Amazon Transcribe
• Azure Speech-to-Text
• Google Speech-to-Text
• IBM Watson Speech-to-Text
• OpenAI Whisper
• Whisper.cpp
• Vosk
• Moonshine
• Picovoice Cheetah
• Picovoice Leopard

Usage

This benchmark has been developed and tested on Ubuntu 22.04.

• Install FFmpeg
• Download datasets.
• Install the requirements:

pip3 install -r requirements.txt

Benchmark Usage

In the following, we provide instructions for running the benchmark for each engine. The supported datasets are: COMMON_VOICE, LIBRI_SPEECH_TEST_CLEAN, LIBRI_SPEECH_TEST_OTHER, TED_LIUM, MLS, VOX_POPULI and FLEURS. The supported languages are: EN, FR, DE, ES, IT, PT_BR, and PT_PT.

To evaluate Punctuation Error Rate, use the --punctuation flag. Use --punctuation-set ${PUNCTUATION_SET} to select which punctuation marks to calculate PER against, where ${PUNCTUATION_SET} is one or more of ., ? and , (default .?).

Amazon Transcribe Instructions

Replace ${DATASET} with one of the supported datasets, ${DATASET_FOLDER} with path to dataset, ${LANGUAGE} with the target language, ${AWS_LOCATION} with the name of the AWS server and ${AWS_PROFILE} with the name of the AWS profile you wish to use.

python3 benchmark.py \
--dataset ${DATASET} \
--dataset-folder ${DATASET_FOLDER} \
--language ${LANGUAGE} \
--engine AMAZON_TRANSCRIBE \
--aws-profile ${AWS_PROFILE} \
--aws-location ${AWS_LOCATION}

Set --engine to AMAZON_TRANSCRIBE_STREAMING to use Amazon Transcribe in streaming mode.

Azure Speech-to-Text Instructions

Replace ${DATASET} with one of the supported datasets, ${DATASET_FOLDER} with path to dataset, ${LANGUAGE} with the target language, ${AZURE_SPEECH_KEY} and ${AZURE_SPEECH_LOCATION} information from your Azure account.

python3 benchmark.py \
--dataset ${DATASET} \
--dataset-folder ${DATASET_FOLDER} \
--language ${LANGUAGE} \
--engine AZURE_SPEECH_TO_TEXT \
--azure-speech-key ${AZURE_SPEECH_KEY}
--azure-speech-location ${AZURE_SPEECH_LOCATION}

Set --engine to AZURE_SPEECH_TO_TEXT_REAL_TIME to use Azure Speech-to-text in streaming mode.

Google Speech-to-Text Instructions

Replace ${DATASET} with one of the supported datasets, ${DATASET_FOLDER} with path to dataset, ${LANGUAGE} with the target language and ${GOOGLE_APPLICATION_CREDENTIALS} with credentials download from Google Cloud Platform.

python3 benchmark.py \
--dataset ${DATASET} \
--dataset-folder ${DATASET_FOLDER} \
--language ${LANGUAGE} \
--engine GOOGLE_SPEECH_TO_TEXT \
--google-application-credentials ${GOOGLE_APPLICATION_CREDENTIALS}

Set --engine to GOOGLE_SPEECH_TO_TEXT_STREAMING to use Google Speech-to-text in streaming mode.

IBM Watson Speech-to-Text Instructions

Replace ${DATASET} with one of the supported datasets, ${DATASET_FOLDER} with path to dataset and ${WATSON_SPEECH_TO_TEXT_API_KEY}/${${WATSON_SPEECH_TO_TEXT_URL}} with credentials from your IBM account. This engine only supports English.

python3 benchmark.py \
--dataset ${DATASET} \
--dataset-folder ${DATASET_FOLDER} \
--language EN \
--engine IBM_WATSON_SPEECH_TO_TEXT \
--watson-speech-to-text-api-key ${WATSON_SPEECH_TO_TEXT_API_KEY}
--watson-speech-to-text-url ${WATSON_SPEECH_TO_TEXT_URL}

OpenAI Whisper Instructions

Replace ${DATASET} with one of the supported datasets, ${DATASET_FOLDER} with path to dataset, ${LANGUAGE} with the target language and ${WHISPER_MODEL} with the whisper model type (WHISPER_TINY, WHISPER_BASE, WHISPER_SMALL, WHISPER_MEDIUM, WHISPER_LARGE_V1, WHISPER_LARGE_V2, WHISPER_LARGE_V3 or WHISPER_LARGE_TURBO)

python3 benchmark.py \
--engine ${WHISPER_MODEL} \
--dataset ${DATASET} \
--language ${LANGUAGE} \
--dataset-folder ${DATASET_FOLDER} \

Whisper.cpp Streaming Instructions

Replace ${DATASET} with one of the supported datasets, ${DATASET_FOLDER} with path to dataset, ${LANGUAGE} with the target language, and ${WHISPER_CPP_MODEL} with the Whisper.cpp streaming model type (WHISPER_CPP_STREAMING_TINY, WHISPER_CPP_STREAMING_BASE, WHISPER_CPP_STREAMING_SMALL, WHISPER_CPP_STREAMING_MEDIUM, WHISPER_CPP_STREAMING_LARGE_V3 or WHISPER_CPP_STREAMING_LARGE_TURBO).

python3 benchmark.py \
--engine ${WHISPER_CPP_MODEL} \
--dataset ${DATASET} \
--dataset-folder ${DATASET_FOLDER}
--language ${LANGUAGE} \

Moonshine Streaming Instructions

Replace ${DATASET} with one of the supported datasets, ${DATASET_FOLDER} with path to dataset and ${MOONSHINE_MODEL} with the Moonshine streaming model type (MOONSHINE_STREAMING_TINY, MOONSHINE_STREAMING_SMALL or MOONSHINE_STREAMING_MEDIUM). This engine only supports English.

python3 benchmark.py \
--engine ${MOONSHINE_MODEL} \
--dataset ${DATASET} \
--dataset-folder ${DATASET_FOLDER}
--language EN \

Vosk Streaming Instructions

Replace ${DATASET} with one of the supported datasets, ${DATASET_FOLDER} with path to dataset and ${VOSK_MODEL} with the Vosk streaming model type (VOSK_STREAMING_SMALL or VOSK_STREAMING_LARGE). This engine only supports English.

python3 benchmark.py \
--engine ${VOSK_MODEL} \
--dataset ${DATASET} \
--dataset-folder ${DATASET_FOLDER} \
--language EN

Picovoice Cheetah Instructions

Replace ${DATASET} with one of the supported datasets, ${DATASET_FOLDER} with path to dataset, ${LANGUAGE} with the target language, and ${PICOVOICE_ACCESS_KEY} with AccessKey obtained from Picovoice Console. By default, the Cheetah English model is used. For non-English languages models replace ${PICOVOICE_MODEL_PATH} with the path to a model file acquired from the Cheetah Github Repo.

python3 benchmark.py \
--engine PICOVOICE_CHEETAH \
--dataset ${DATASET} \
--dataset-folder ${DATASET_FOLDER} \
--language ${LANGUAGE} \
--picovoice-access-key ${PICOVOICE_ACCESS_KEY} \
--picovoice-model-path ${PICOVOICE_MODEL_PATH}

Picovoice Leopard Instructions

Replace ${DATASET} with one of the supported datasets, ${DATASET_FOLDER} with path to dataset, ${LANGUAGE} with the target language, and ${PICOVOICE_ACCESS_KEY} with AccessKey obtained from Picovoice Console. If benchmarking a non-English language, include --picovoice-model-path and replace ${PICOVOICE_MODEL_PATH} with the path to a model file acquired from the Leopard Github Repo.

python3 benchmark.py \
--engine PICOVOICE_LEOPARD \
--dataset ${DATASET} \
--dataset-folder ${DATASET_FOLDER} \
--language ${LANGUAGE} \
--picovoice-access-key ${PICOVOICE_ACCESS_KEY} \
--picovoice-model-path ${PICOVOICE_MODEL_PATH}

Latency Benchmark Usage

In the following, we provide instructions for running the latency benchmark for each streaming engine. To run the benchmark, generate word timing alignment information for one of the supported datasets by following the instructions found here. Replace ${DATASET_PATH} with the path to the outputted folder from this process in the following commands.

Amazon Transcribe Instructions

Replace ${DATASET_FOLDER} with the path to an aligned dataset, ${LANGUAGE} with the target language, ${AWS_LOCATION} with the name of the AWS server and ${AWS_PROFILE} with the name of the AWS profile you wish to use.

python3 benchmark_latency.py \
--engine AMAZON_TRANSCRIBE_STREAMING \
--dataset-folder ${DATASET_FOLDER} \
--language ${LANGUAGE} \
--aws-profile ${AWS_PROFILE} \
--aws-location ${AWS_LOCATION}

Azure Speech-to-Text Instructions

Replace ${DATASET_FOLDER} with the path to an aligned dataset, ${LANGUAGE} with the target language, ${AZURE_SPEECH_KEY} and ${AZURE_SPEECH_LOCATION} information from your Azure account.

python3 benchmark_latency.py \
--engine AZURE_SPEECH_TO_TEXT_REAL_TIME \
--dataset-folder ${DATASET_FOLDER} \
--language ${LANGUAGE} \
--azure-speech-key ${AZURE_SPEECH_KEY}
--azure-speech-location ${AZURE_SPEECH_LOCATION}

Google Speech-to-Text Instructions

Replace ${DATASET_FOLDER} with the path to an aligned dataset, ${LANGUAGE} with the target language, and ${GOOGLE_APPLICATION_CREDENTIALS} with credentials download from Google Cloud Platform.

python3 benchmark_latency.py \
--engine GOOGLE_SPEECH_TO_TEXT_STREAMING \
--dataset-folder ${DATASET_FOLDER} \
--language ${LANGUAGE} \
--google-application-credentials ${GOOGLE_APPLICATION_CREDENTIALS}

Whisper.cpp Streaming Instructions

Replace ${DATASET_FOLDER} with the path to an aligned dataset, ${LANGUAGE} with the target language, and ${WHISPER_CPP_MODEL} with the Whisper.cpp streaming model type (WHISPER_CPP_STREAMING_TINY, WHISPER_CPP_STREAMING_BASE, WHISPER_CPP_STREAMING_SMALL, WHISPER_CPP_STREAMING_MEDIUM, WHISPER_CPP_STREAMING_LARGE_V3 or WHISPER_CPP_STREAMING_LARGE_TURBO).

python3 benchmark_latency.py \
--engine ${WHISPER_CPP_MODEL} \
--dataset-folder ${DATASET_FOLDER} \
--language ${LANGUAGE}

Moonshine Streaming Instructions

Replace ${DATASET_FOLDER} with the path to an aligned dataset and ${MOONSHINE_MODEL} with the Moonshine streaming model type (MOONSHINE_STREAMING_TINY, MOONSHINE_STREAMING_SMALL or MOONSHINE_STREAMING_MEDIUM). This engine only supports English.

python3 benchmark_latency.py \
--engine ${MOONSHINE_MODEL} \
--dataset-folder ${DATASET_FOLDER} \
--language EN

Vosk Streaming Instructions

Replace ${DATASET_FOLDER} with the path to an aligned dataset and ${VOSK_MODEL} with the Vosk streaming model type (VOSK_STREAMING_SMALL or VOSK_STREAMING_LARGE). This engine only supports English.

python3 benchmark_latency.py \
--engine ${VOSK_MODEL} \
--dataset-folder ${DATASET_FOLDER} \
--language EN

Picovoice Cheetah Instructions

Replace ${DATASET_FOLDER} with the path to an aligned dataset, ${LANGUAGE} with the target language, and ${PICOVOICE_ACCESS_KEY} with an AccessKey obtained from Picovoice Console. By default, the Cheetah English model is used. For non-English languages models replace ${PICOVOICE_MODEL_PATH} with the path to a model file obtained from the Cheetah Github Repo.

python3 benchmark_latency.py \
--engine PICOVOICE_CHEETAH \
--dataset-folder ${DATASET_FOLDER} \
--language ${LANGUAGE} \
--picovoice-access-key ${PICOVOICE_ACCESS_KEY} \
--picovoice-model-path ${PICOVOICE_MODEL_PATH}

Results

English

Batch Engines Word Error Rate

Engine	LibriSpeech test-clean	LibriSpeech test-other	TED-LIUM	CommonVoice	Average
Amazon Transcribe	2.3%	4.6%	4.0%	6.4%	4.3%
Azure Speech-to-Text	2.9%	6.0%	4.6%	8.4%	5.5%
Google Speech-to-Text	5.3%	10.5%	5.5%	14.3%	8.9%
IBM Watson Speech-to-Text	10.9%	26.2%	11.7%	39.4%	22.0%
Whisper Large V3	3.7%	5.4%	4.6%	9.0%	5.7%
Whisper Medium	3.3%	6.2%	4.6%	10.2%	6.1%
Whisper Small	3.3%	7.2%	4.8%	12.7%	7.0%
Whisper Base	4.3%	10.4%	5.4%	17.9%	9.5%
Whisper Tiny	5.9%	13.8%	6.6%	24.4%	12.7%
Picovoice Leopard	5.1%	11.1%	6.4%	16.1%	9.7%

Streaming Engines Word Error Rate

Engine	LibriSpeech test-clean	LibriSpeech test-other	TED-LIUM	CommonVoice	Average
Amazon Transcribe Streaming	2.6%	5.5%	4.8%	9.4%	5.6%
Azure Speech-to-Text Real Time	4.9%	8.5%	8.7%	10.7%	8.2%
Google Speech-to-Text Streaming	8.6%	14.3%	7.9%	16.8%	11.9%
Whisper.cpp Streaming Tiny	12.7%	23.3%	16.0%	37.5%	22.4%
Whisper.cpp Streaming Base	11.9%	19.9%	14.2%	33.2%	19.8%
Vosk Small	9.9%	21.0%	10.7%	32.1%	18.4%
Vosk Large	5.4%	12.7%	6.6%	21.4%	11.5%
Moonshine Tiny	11.8%	28.7%	12.5%	42.4%	23.9%
Moonshine Small	7.0%	15.0%	6.9%	24.8%	13.4%
Moonshine Medium	5.9%	11.4%	6.5%	18.7%	10.6%
Picovoice Cheetah	5.4%	11.4%	6.4%	17.0%	10.1%

Streaming Engines Punctuation Error Rate

Engine	CommonVoice	Fleurs	VoxPopuli	Average
Amazon Transcribe Streaming	13.2%	24.4%	35.5%	24.4%
Azure Speech-to-Text Real Time	5.6%	17.6%	25.9%	16.4%
Google Speech-to-Text Streaming	20.2%	42.7%	45.0%	36.0%
Whisper.cpp Streaming Tiny	41.2%	57.9%	62.1%	53.7%
Whisper.cpp Streaming Base	43.2%	56.4%	62.8%	54.1%
Moonshine Tiny	21.0%	46.5%	59.1%	42.2%
Moonshine Small	30.5%	45.3%	59.5%	45.1%
Moonshine Medium	32.3%	46.1%	55.4%	44.6%
Picovoice Cheetah	6.5%	14.4%	27.4%	16.1%

Core-Hour & Model Size

To obtain these results, we ran the benchmark across the entire LibriSpeech test-clean dataset and recorded the processing time. The measurement is carried out on an Ubuntu 22.04 machine with AMD CPU (AMD Ryzen 9 5900X (12) @ 3.70GHz), 64 GB of RAM, and NVMe storage, using 10 cores simultaneously. We omit Whisper Large from this benchmark.

Engine	Core-Hour	Model Size / MB
Whisper Medium	1.52	1457
Whisper Small	0.99	462
Whisper Base	0.32	139
Whisper Tiny	0.16	73
Whisper.cpp Streaming Base	1.67	139
Whisper.cpp Streaming Tiny	0.77	73
Vosk Large	0.34	2733
Vosk Small	0.12	68
Moonshine Streaming Tiny	1.03	49
Moonshine Streaming Small	2.22	158
Moonshine Streaming Medium	3.36	290
Picovoice Leopard	0.026	37
Picovoice Cheetah	0.083	34

Word Emission Latency

To obtain these results, we used 100 randomly selected files from the LibriSpeech test-clean dataset.

Engine	Latency (ms)
Azure Speech-to-Text Real-time	530
Amazon Transcribe Streaming	920
Google Speech-to-Text Streaming	830
Whisper.cpp Streaming Tiny	1240
Whisper.cpp Streaming Base	1240
Vosk Small	920
Vosk Large	2000
Moonshine Tiny	780
Moonshine Small	650
Moonshine Medium	640
Picovoice Cheetah	590

French

Batch Engines Word Error Rate

Engine	CommonVoice	Multilingual LibriSpeech	VoxPopuli	Average
Amazon Transcribe	6.0%	4.4%	8.6%	6.3%
Azure Speech-to-Text	11.1%	9.0%	11.8%	10.6%
Google Speech-to-Text	14.3%	14.2%	15.1%	14.5%
Whisper Large	9.3%	4.6%	10.9%	8.3%
Whisper Medium	13.1%	8.6%	12.1%	11.3%
Whisper Small	19.2%	13.5%	15.3%	16.0%
Whisper Base	35.4%	24.4%	23.3%	27.7%
Whisper Tiny	49.8%	36.2%	32.1%	39.4%
Picovoice Leopard	15.9%	19.2%	17.5%	17.5%

Streaming Engines Word Error Rate

Engine	CommonVoice	Multilingual LibriSpeech	VoxPopuli	Average
Amazon Transcribe Streaming	9.8%	7.7%	10.4%	9.3%
Azure Speech-to-Text Real Time	13.3%	14.1%	20.0%	15.8%
Google Speech-to-Text Streaming	16.9%	19.4%	19.1%	18.5%
Picovoice Cheetah	14.1%	13.2%	13.5%	13.6%

Streaming Engines Punctuation Error Rate

Engine	CommonVoice	Fleurs	VoxPopuli	Average
Amazon Transcribe Streaming	7.4%	17.0%	21.9%	15.4%
Azure Speech-to-Text Real Time	6.7%	18.8%	28.4%	18.0%
Google Speech-to-Text Streaming	26.4%	22.3%	28.6%	25.8%
Picovoice Cheetah	7.0%	19.6%	29.9%	18.8%

German

Batch Engines Word Error Rate

Engine	CommonVoice	Multilingual LibriSpeech	VoxPopuli	Average
Amazon Transcribe	5.3%	2.9%	14.6%	7.6%
Azure Speech-to-Text	6.9%	5.4%	13.1%	8.5%
Google Speech-to-Text	9.2%	13.9%	17.2%	13.4%
Whisper Large	5.3%	4.4%	12.5%	7.4%
Whisper Medium	8.3%	7.6%	13.5%	9.8%
Whisper Small	13.8%	11.2%	16.2%	13.7%
Whisper Base	26.9%	19.8%	24.0%	23.6%
Whisper Tiny	39.5%	28.6%	33.0%	33.7%
Picovoice Leopard	8.2%	11.6%	23.6%	14.5%

Streaming Engines Word Error Rate

Engine	CommonVoice	Multilingual LibriSpeech	VoxPopuli	Average
Amazon Transcribe Streaming	6.4%	6.8%	12.1%	8.4%
Azure Speech-to-Text Real Time	6.9%	6.6%	16.5%	10.0%
Google Speech-to-Text Streaming	10.7%	16.7%	20.9%	16.1%
Picovoice Cheetah	9.4%	10.4%	15.8%	11.9%

Streaming Engines Punctuation Error Rate

Engine	CommonVoice	Fleurs	VoxPopuli	Average
Amazon Transcribe Streaming	3.1%	23.5%	20.6%	15.7%
Azure Speech-to-Text Real Time	2.3%	28.4%	25.8%	18.8%
Google Speech-to-Text Streaming	15.8%	27.1%	28.6%	23.8%
Picovoice Cheetah	3.0%	22.6%	30.7%	18.8%

Italian

Batch Engines Word Error Rate

Engine	CommonVoice	Multilingual LibriSpeech	VoxPopuli	Average
Amazon Transcribe	4.1%	9.1%	16.1%	9.8%
Azure Speech-to-Text	5.8%	14.0%	17.8%	12.5%
Google Speech-to-Text	5.5%	19.6%	18.7%	14.6%
Whisper Large	4.9%	8.8%	21.8%	11.8%
Whisper Medium	8.7%	14.9%	19.3%	14.3%
Whisper Small	15.4%	20.6%	22.7%	19.6%
Whisper Base	32.3%	31.6%	31.6%	31.8%
Whisper Tiny	48.1%	43.3%	43.5%	45.0%
Picovoice Leopard	13.0%	27.7%	22.2%	21.0%

Streaming Engines Word Error Rate

Engine	CommonVoice	Multilingual LibriSpeech	VoxPopuli	Average
Amazon Transcribe Streaming	5.2%	12.6%	16.6%	11.5%
Azure Speech-to-Text Real Time	8.2%	21.3%	26.1%	18.5%
Google Speech-to-Text Streaming	6.6%	25.2%	22.2%	18.0%
Picovoice Cheetah	8.3%	16.0%	18.7%	14.3%

Streaming Engines Punctuation Error Rate

Engine	CommonVoice	Fleurs	VoxPopuli	Average
Amazon Transcribe Streaming	5.0%	46.4%	34.6%	28.7%
Azure Speech-to-Text Real Time	5.5%	28.8%	40.8%	25.0%
Google Speech-to-Text Streaming	27.4%	23.5%	46.0%	32.3%
Picovoice Cheetah	4.3%	35.7%	46.4%	28.8%

Spanish

Batch Engines Word Error Rate

Engine	CommonVoice	Multilingual LibriSpeech	VoxPopuli	Average
Amazon Transcribe	3.9%	3.3%	8.7%	5.3%
Azure Speech-to-Text	6.3%	5.8%	9.4%	7.2%
Google Speech-to-Text	6.6%	9.2%	11.6%	9.1%
Whisper Large	4.0%	2.9%	9.7%	5.5%
Whisper Medium	6.2%	4.8%	9.7%	6.9%
Whisper Small	9.8%	7.7%	11.4%	9.6%
Whisper Base	20.2%	13.0%	15.3%	16.2%
Whisper Tiny	33.3%	20.6%	22.7%	25.5%
Picovoice Leopard	7.6%	14.9%	14.1%	12.2%

Streaming Engines Word Error Rate

Engine	CommonVoice	Multilingual LibriSpeech	VoxPopuli	Average
Amazon Transcribe Streaming	5.3%	5.0%	8.9%	6.4%
Azure Speech-to-Text Real Time	7.1%	7.1%	13.9%	9.4%
Google Speech-to-Text Streaming	7.4%	11.3%	16.2%	11.6%
Picovoice Cheetah	7.8%	7.3%	10.6%	8.6%

Streaming Engines Punctuation Error Rate

Engine	CommonVoice	Fleurs	VoxPopuli	Average
Amazon Transcribe Streaming	5.7%	21.2%	23.9%	16.9%
Azure Speech-to-Text Real Time	3.9%	20.3%	27.2%	17.1%
Google Speech-to-Text Streaming	58.6%	45.1%	41.9%	48.5%
Picovoice Cheetah	4.0%	21.7%	35.1%	20.3%

Portuguese

For Amazon Transcribe, Azure Speech-to-Text, and Google Speech-to-Text, we report results with the language set to PT-BR, as this achieves better results compared to PT-PT across all engines.

Batch Engines Word Error Rate

Engine	CommonVoice	Multilingual LibriSpeech	Average
Amazon Transcribe	5.4%	7.8%	6.6%
Azure Speech-to-Text	7.4%	9.0%	8.2%
Google Speech-to-Text	8.8%	14.2%	11.5%
Whisper Large	5.9%	5.4%	5.7%
Whisper Medium	9.6%	8.1%	8.9%
Whisper Small	15.6%	13.0%	14.3%
Whisper Base	31.2%	22.7%	27.0%
Whisper Tiny	47.7%	34.6%	41.2%
Picovoice Leopard	17.1%	20.0%	18.6%

Streaming Engines Word Error Rate

Engine	CommonVoice	Multilingual LibriSpeech	Average
Amazon Transcribe Streaming	7.0%	9.0%	8.0%
Azure Speech-to-Text Real Time	8.3%	11.0%	9.7%
Google Speech-to-Text Streaming	9.1%	16.5%	12.8%
Picovoice Cheetah	10.6%	14.0%	12.3%

Streaming Engines Punctuation Error Rate

Engine	CommonVoice	Fleurs	Average
Amazon Transcribe Streaming	11.1%	27.6%	19.4%
Azure Speech-to-Text Real Time	13.3%	28.6%	21.0%
Google Speech-to-Text Streaming	30.9%	31.9%	31.4%
Picovoice Cheetah	11.2%	31.6%	21.4%