ruby

whispercpp

Ruby bindings for whisper.cpp, an interface of automatic speech recognition model.

Installation

Install the gem and add to the application's Gemfile by executing:

$ bundle add whispercpp

If bundler is not being used to manage dependencies, install the gem by executing:

$ gem install whispercpp

Usage

require "whisper"

whisper = Whisper::Context.new("path/to/model.bin")

params = Whisper::Params.new
params.language = "en"
params.offset = 10_000
params.duration = 60_000
params.max_text_tokens = 300
params.translate = true
params.print_timestamps = false
params.initial_prompt = "Initial prompt here."

whisper.transcribe("path/to/audio.wav", params) do |whole_text|
  puts whole_text
end

Preparing model

Use script to download model file(s):

git clone https://github.com/ggerganov/whisper.cpp.git
cd whisper.cpp
sh ./models/download-ggml-model.sh base.en

There are some types of models. See models page for details.

Preparing audio file

Currently, whisper.cpp accepts only 16-bit WAV files.

API

Once Whisper::Context#transcribe called, you can retrieve segments by #each_segment:

def format_time(time_ms)
  sec, decimal_part = time_ms.divmod(1000)
  min, sec = sec.divmod(60)
  hour, min = min.divmod(60)
  "%02d:%02d:%02d.%03d" % [hour, min, sec, decimal_part]
end

whisper.transcribe("path/to/audio.wav", params)

whisper.each_segment.with_index do |segment, index|
  line = "[%{nth}: %{st} --> %{ed}] %{text}" % {
    nth: index + 1,
    st: format_time(segment.start_time),
    ed: format_time(segment.end_time),
    text: segment.text
  }
  line << " (speaker turned)" if segment.speaker_next_turn?
  puts line
end

You can also add hook to params called on new segment:

def format_time(time_ms)
  sec, decimal_part = time_ms.divmod(1000)
  min, sec = sec.divmod(60)
  hour, min = min.divmod(60)
  "%02d:%02d:%02d.%03d" % [hour, min, sec, decimal_part]
end

# Add hook before calling #transcribe
params.on_new_segment do |segment|
  line = "[%{st} --> %{ed}] %{text}" % {
    st: format_time(segment.start_time),
    ed: format_time(segment.end_time),
    text: segment.text
  }
  line << " (speaker turned)" if segment.speaker_next_turn?
  puts line
end

whisper.transcribe("path/to/audio.wav", params)

You can see model information:

whisper = Whisper::Context.new("path/to/model.bin")
model = whisper.model

model.n_vocab # => 51864
model.n_audio_ctx # => 1500
model.n_audio_state # => 512
model.n_audio_head # => 8
model.n_audio_layer # => 6
model.n_text_ctx # => 448
model.n_text_state # => 512
model.n_text_head # => 8
model.n_text_layer # => 6
model.n_mels # => 80
model.ftype # => 1
model.type # => "base"

You can set log callback:

prefix = "[MyApp] "
log_callback = ->(level, buffer, user_data) {
  case level
  when Whisper::LOG_LEVEL_NONE
    puts "#{user_data}none: #{buffer}"
  when Whisper::LOG_LEVEL_INFO
    puts "#{user_data}info: #{buffer}"
  when Whisper::LOG_LEVEL_WARN
    puts "#{user_data}warn: #{buffer}"
  when Whisper::LOG_LEVEL_ERROR
    puts "#{user_data}error: #{buffer}"
  when Whisper::LOG_LEVEL_DEBUG
    puts "#{user_data}debug: #{buffer}"
  when Whisper::LOG_LEVEL_CONT
    puts "#{user_data}same to previous: #{buffer}"
  end
}
Whisper.log_set log_callback, prefix

Using this feature, you are also able to suppress log:

Whisper.log_set ->(level, buffer, user_data) {
  # do nothing
}, nil
Whisper::Context.new(MODEL)

You can also call Whisper::Context#full and #full_parallel with a Ruby array as samples. Although #transcribe with audio file path is recommended because it extracts PCM samples in C++ and is fast, #full and #full_parallel give you flexibility.

require "whisper"
require "wavefile"

reader = WaveFile::Reader.new("path/to/audio.wav", WaveFile::Format.new(:mono, :float, 16000))
samples = reader.enum_for(:each_buffer).map(&:samples).flatten

whisper = Whisper::Context.new("path/to/model.bin")
whisper.full(Whisper::Params.new, samples)
whisper.each_segment do |segment|
  puts segment.text
end

The second argument samples may be an array, an object with length method, or a MemoryView. If you can prepare audio data as C array and export it as a MemoryView, whispercpp accepts and works with it with zero copy.

License

The same to whisper.cpp.