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https://github.com/huggingface/candle.git
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Read wav files.
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@ -21,6 +21,7 @@ clap = { version = "4.2.4", features = ["derive"] }
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rand = "0.8.5"
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tokenizers = { version = "0.13.3", default-features=false, features=["onig"] }
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tokio = { version = "1.28.2", features = ["macros", "rt-multi-thread"] }
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wav = "1.0.0"
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[features]
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default = ["cuda"]
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@ -1,13 +1,16 @@
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// Audio processing code, adapted from whisper.cpp
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// https://github.com/ggerganov/whisper.cpp
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const WHISPER_SAMPLE_RATE: usize = 16000;
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const WHISPER_N_FFT: usize = 400;
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const WHISPER_N_MEL: usize = 80;
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const WHISPER_HOP_LENGTH: usize = 160;
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const WHISPER_CHUNK_SIZE: usize = 30;
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pub const WHISPER_SAMPLE_RATE: usize = 16000;
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pub const WHISPER_N_FFT: usize = 400;
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pub const WHISPER_N_MEL: usize = 80;
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pub const WHISPER_HOP_LENGTH: usize = 160;
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pub const WHISPER_CHUNK_SIZE: usize = 30;
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trait Float: num_traits::Float + num_traits::FloatConst + num_traits::NumAssign {}
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pub trait Float: num_traits::Float + num_traits::FloatConst + num_traits::NumAssign {}
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impl Float for f32 {}
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impl Float for f64 {}
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// https://github.com/ggerganov/whisper.cpp/blob/4774d2feb01a772a15de81ffc34b34a1f294f020/whisper.cpp#L2357
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fn fft<T: Float>(inp: &[T]) -> Vec<T> {
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@ -203,7 +206,7 @@ fn log_mel_spectrogram_<T: Float>(
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mel
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}
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fn pcm_to_mel<T: Float>(samples: &[T], filters: &[T]) -> anyhow::Result<Vec<T>> {
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pub fn pcm_to_mel<T: Float>(samples: &[T], filters: &[T]) -> anyhow::Result<Vec<T>> {
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if filters.len() != WHISPER_N_MEL * WHISPER_N_FFT {
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anyhow::bail!(
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"unexpected filter length {} (n_mel: {}, n_fft: {})",
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@ -40,33 +40,6 @@ const EOT_TOKEN: u32 = 50256;
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const NO_SPEECH_TOKEN: u32 = 50361;
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const NO_TIMESTAMP_TOKEN: u32 = 50362;
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#[derive(Parser, Debug)]
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#[command(author, version, about, long_about = None)]
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struct Args {
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/// Run on CPU rather than on GPU.
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#[arg(long)]
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cpu: bool,
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#[arg(long)]
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weights: String,
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#[arg(long)]
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input: String,
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#[arg(long)]
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tokenizer_config: String,
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/// The seed to use when generating random samples.
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#[arg(long, default_value_t = 299792458)]
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seed: u64,
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#[arg(
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long,
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default_value = "candle-examples/examples/whisper/mel_filters.safetensors"
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)]
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filters: String,
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}
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#[derive(Debug, Clone)]
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struct DecodingResult {
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tokens: Vec<u32>,
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@ -184,6 +157,35 @@ impl Decode {
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}
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}
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#[derive(Parser, Debug)]
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#[command(author, version, about, long_about = None)]
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struct Args {
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/// Run on CPU rather than on GPU.
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#[arg(long)]
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cpu: bool,
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#[arg(long)]
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weights: String,
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/// The input to be processed, in wav formats.
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#[arg(long)]
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input: String,
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#[arg(long)]
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tokenizer_config: String,
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/// The seed to use when generating random samples.
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#[arg(long, default_value_t = 299792458)]
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seed: u64,
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/// The mel filters in safetensors format.
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#[arg(
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long,
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default_value = "candle-examples/examples/whisper/mel_filters.safetensors"
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)]
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filters: String,
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}
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fn main() -> Result<()> {
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let args = Args::parse();
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let device = if args.cpu {
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@ -195,14 +197,28 @@ fn main() -> Result<()> {
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let tokenizer = Tokenizer::from_file(args.tokenizer_config).map_err(E::msg)?;
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let filters = unsafe { candle::safetensors::MmapedFile::new(args.filters)? };
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let filters = filters.deserialize()?;
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let filters = filters.tensor("mel_80", &device)?;
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println!("loaded mel filters {:?}", filters.shape());
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let mel_filters = unsafe { candle::safetensors::MmapedFile::new(args.filters)? };
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let mel_filters = mel_filters.deserialize()?;
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let mel_filters = mel_filters.tensor("mel_80", &device)?;
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println!("loaded mel filters {:?}", mel_filters.shape());
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let mel_filters = mel_filters.flatten_all()?.to_vec1::<f32>()?;
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let input = unsafe { candle::safetensors::MmapedFile::new(args.input)? };
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let input = input.deserialize()?;
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let mel = input.tensor("mel", &device)?;
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let mut input = std::fs::File::open(args.input)?;
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let (header, data) = wav::read(&mut input)?;
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println!("loaded wav data: {header:?}");
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if header.sampling_rate != audio::WHISPER_SAMPLE_RATE as u32 {
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anyhow::bail!(
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"wav file must have a {} sampling rate",
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audio::WHISPER_SAMPLE_RATE
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)
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}
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let data = data.as_sixteen().expect("expected 16 bit wav file");
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let pcm_data: Vec<_> = data[..data.len() / header.channel_count as usize]
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.iter()
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.map(|v| *v as f32 / 32768.)
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.collect();
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let mel = audio::pcm_to_mel(&pcm_data, &mel_filters)?;
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let mel = Tensor::new(&mel[..], &device)?;
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println!("loaded mel: {:?}", mel.dims());
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let weights = unsafe { candle::safetensors::MmapedFile::new(args.weights)? };
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