Speech-to-Text Benchmark

Made in Vancouver, Canada by Picovoice

This is a minimalist and extensible framework for benchmarking different speech-to-text engines. It has been developed and tested on Ubuntu 18.04 with Python3.6.


This framework has been developed by Picovoice as part of the project
Cheetah. Cheetah is Picovoice's speech-to-text engine specifically designed for IoT applications.
Deep learning has been the main driver in recent improvements in speech recognition. But due to
stringent compute/storage limitations of IoT platforms it is most beneficial to
the cloud-based engines. Picovoice's proprietary deep learning technology enables transferring
these improvements to IoT platforms with much lower CPU/memory footprint. The goal is to be able
to run Cheetah on any platform with a C Compiler and a few MB of memory.

This framework enabled us to measure our progress in improving Cheetah and also compare its performance
with existing solutions. Our hope is that this can be useful to the research community as well.


Mozilla Common Voice dataset is used for benchmarking.
Only the valid test portion is used to allow engines to use train portion of the dataset.
Since the dataset is community-verified we only
use examples that have no downvotes and at least two upvotes. This provides us with approximately
2.5 hours of test speech data. Common Voice dataset is a useful and challenging dataset
as (1) it contains a variety of different accents and (2) recordings are performed in a variety of
acoustic environments and are not necessarily clean or near-field.


Three different metrics are measured.

Word Error Rate

Word error rate is defined as the Levenstein distance
between words in reference transcript and words in the output of the speech-to-text engine to the number of
words in reference transcript.

Real Time Factor

Real-time factor (RTF) is measured as the ratio of CPU (processing) time in seconds to the length
of the input speech file in seconds. A speech-to-text engine with lower RTF is computationally more efficient


The amount of heap memory used.

Speech-to-Text Engines

All engines below run fully on-device (no cloud connection needed).

Mozilla DeepSpeech

Mozilla DeepSpeech is an open-source implementation of
Baidu's DeepSpeech by Mozilla. It can run with or without a language model.
The engine is not yet supported on embedded (mobile/IoT) platforms.

Picovoice Cheetah

Cheetah is a speech-to-text engine developed using Picovoice's proprietary deep learning
technology. It works offline and is supported on a growing number of embedded platforms including
Android, iOS, and Raspberry Pi.


PocketSphinx works offline and can run on embedded platforms
such as Raspberry Pi.


Below is information on how to use this framework to benchmark engines mentioned above. First, make sure that
you have already installed DeepSpeech and PocketSphinx on your machine following instructions on their
official pages. Then download Common Voice dataset.

Word Error Rate Measurement

WER can be measured by running the following command from the root of the repository.
COMMON_VOICE_PATH is the absolute path to the root directory of Common Voice
dataset. DEEP_SPEECH_MODELS_PATH is the absolute path to Mozilla DeepSpeech's model folder.

python benchmark.py --dataset_root COMMON_VOICE_PATH --deep_speech_model_path DEEP_SPEECH_MODELS_PATH/output_graph.pb \
--deep_speech_alphabet_path DEEP_SPEECH_MODELS_PATH/alphabet.txt

The above prints the WER for different engines in console. In order to get results with language
modeling enabled for DeepSpeech use the following

python benchmark.py --dataset_root COMMON_VOICE_PATH --deep_speech_model_path DEEP_SPEECH_MODELS_PATH/output_graph.pb \
--deep_speech_alphabet_path DEEP_SPEECH_MODELS_PATH/alphabet.txt --deep_speech_language_model_path DEEP_SPEECH_MODELS_PATH/lm.binary \
--deep_speech_trie_path DEEP_SPEECH_MODELS_PATH/trie

Real Time Factor Measurement

time command is used to measure execution time of different engines for a given audio file and then divide
the CPU time by audio length. In order to measure execution time for Cheetah run

time resources/cheetah/pv_cheetah_app PATH_TO_WAV_FILE resources/cheetah/cheetah_params.pv resources/cheetah/cheetah_linux_eval.lic

The output should have the following format (values will be different)

real	0m4.961s
user	0m4.936s
sys	0m0.024s

then divide user by length of the audio file in seconds. The user is the actual CPU time spent in the program.

For DeepSpeech without language model decoding

time deepspeech DEEP_SPEECH_MODELS_PATH/output_graph.pb PATH_TO_WAV_FILE DEEP_SPEECH_MODELS_PATH/alphabet.txt

For DeepSpeech with language model decoding

time deepspeech DEEP_SPEECH_MODELS_PATH/output_graph.pb PATH_TO_WAV_FILE DEEP_SPEECH_MODELS_PATH/alphabet.txt \

Finally for PocketSphinx

time pocketsphinx_continuous -infile PATH_TO_WAV_FILE

Memory Usage measurement

Valgrind's massif
tool is used to measure heap memory usage. For example

valgrind --tool=massif pocketsphinx_continuous -infile PATH_TO_WAV_FILE

It creates a file with naming like massif.out.XXXX. The file can be read using

ms_print massif.out.XXXX


Below results are obtained by following the steps above. The benchmarking is performed on a laptop running
Ubuntu 18.04 with 8 GB of RAM and Intel i7-4510U CPU running at 2GHz. Furthermore, for embedded
runtime measurements, Raspberry Pi 3 and Raspberry Pi zero are used. DeepSpeech does not officially
support running on Raspberry Pi and hence its corresponding columns are marked with N/A. WER refers to
word error rate and RTF refers to real time factor.

Engine WER RTF (Laptop) RTF (Raspberry Pi 3) RTF (Raspberry Pi Zero) Memory
Mozilla DeepSpeech 0.38 2.9 N/A N/A 1966 MB
Mozilla DeepSpeech with LM 0.3 2.99 N/A N/A 2230 MB
Picovoice Cheetah 0.32 0.03 0.25 1.8 5.6 MB
PocketSphinx 0.55 0.32 1.87 2.04 97.8 MB

Cheetah achieves an accuracy very close to the best performing system, DeepSpeech with language model (0.3 vs 0.32 WER).
But it is 100 times faster and consumes 398 times less memory. This enables Cheetah to run on small commodity
embedded platforms such as Raspberry Pi while delivering the benefits of large models that need much more
compute/memory resources.