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e04c789230
* Add the quantized-whisper model. * Quantized the whisper model. * Adapt the whisper example to handle quantization. * Add the quantized flag. * Load the proper weights.
140 lines
3.8 KiB
Rust
140 lines
3.8 KiB
Rust
use candle::{IndexOp, Result, Tensor, D};
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use tokenizers::Tokenizer;
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const LANGUAGES: [(&str, &str); 99] = [
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("en", "english"),
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("zh", "chinese"),
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("de", "german"),
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("es", "spanish"),
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("ru", "russian"),
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("ko", "korean"),
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("fr", "french"),
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("ja", "japanese"),
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("pt", "portuguese"),
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("tr", "turkish"),
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("pl", "polish"),
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("ca", "catalan"),
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("nl", "dutch"),
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("ar", "arabic"),
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("sv", "swedish"),
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("it", "italian"),
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("id", "indonesian"),
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("hi", "hindi"),
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("fi", "finnish"),
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("vi", "vietnamese"),
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("he", "hebrew"),
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("uk", "ukrainian"),
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("el", "greek"),
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("ms", "malay"),
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("cs", "czech"),
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("ro", "romanian"),
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("da", "danish"),
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("hu", "hungarian"),
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("ta", "tamil"),
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("no", "norwegian"),
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("th", "thai"),
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("ur", "urdu"),
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("hr", "croatian"),
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("bg", "bulgarian"),
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("lt", "lithuanian"),
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("la", "latin"),
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("mi", "maori"),
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("ml", "malayalam"),
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("cy", "welsh"),
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("sk", "slovak"),
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("te", "telugu"),
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("fa", "persian"),
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("lv", "latvian"),
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("bn", "bengali"),
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("sr", "serbian"),
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("az", "azerbaijani"),
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("sl", "slovenian"),
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("kn", "kannada"),
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("et", "estonian"),
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("mk", "macedonian"),
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("br", "breton"),
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("eu", "basque"),
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("is", "icelandic"),
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("hy", "armenian"),
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("ne", "nepali"),
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("mn", "mongolian"),
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("bs", "bosnian"),
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("kk", "kazakh"),
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("sq", "albanian"),
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("sw", "swahili"),
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("gl", "galician"),
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("mr", "marathi"),
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("pa", "punjabi"),
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("si", "sinhala"),
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("km", "khmer"),
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("sn", "shona"),
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("yo", "yoruba"),
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("so", "somali"),
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("af", "afrikaans"),
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("oc", "occitan"),
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("ka", "georgian"),
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("be", "belarusian"),
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("tg", "tajik"),
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("sd", "sindhi"),
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("gu", "gujarati"),
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("am", "amharic"),
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("yi", "yiddish"),
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("lo", "lao"),
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("uz", "uzbek"),
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("fo", "faroese"),
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("ht", "haitian creole"),
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("ps", "pashto"),
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("tk", "turkmen"),
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("nn", "nynorsk"),
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("mt", "maltese"),
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("sa", "sanskrit"),
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("lb", "luxembourgish"),
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("my", "myanmar"),
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("bo", "tibetan"),
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("tl", "tagalog"),
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("mg", "malagasy"),
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("as", "assamese"),
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("tt", "tatar"),
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("haw", "hawaiian"),
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("ln", "lingala"),
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("ha", "hausa"),
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("ba", "bashkir"),
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("jw", "javanese"),
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("su", "sundanese"),
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];
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/// Returns the token id for the selected language.
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pub fn detect_language(
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model: &mut super::Model,
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tokenizer: &Tokenizer,
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mel: &Tensor,
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) -> Result<u32> {
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let (_bsize, _, seq_len) = mel.dims3()?;
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let mel = mel.narrow(
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2,
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0,
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usize::min(seq_len, model.config().max_source_positions),
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)?;
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let device = mel.device();
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let language_token_ids = LANGUAGES
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.iter()
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.map(|(t, _)| crate::token_id(tokenizer, &format!("<|{t}|>")))
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.collect::<Result<Vec<_>>>()?;
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let sot_token = crate::token_id(tokenizer, crate::m::SOT_TOKEN)?;
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let audio_features = model.encoder_forward(&mel, true)?;
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let tokens = Tensor::new(&[[sot_token]], device)?;
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let language_token_ids = Tensor::new(language_token_ids.as_slice(), device)?;
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let ys = model.decoder_forward(&tokens, &audio_features, true)?;
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let logits = model.decoder_final_linear(&ys.i(..1)?)?.i(0)?.i(0)?;
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let logits = logits.index_select(&language_token_ids, 0)?;
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let probs = candle_nn::ops::softmax(&logits, D::Minus1)?;
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let probs = probs.to_vec1::<f32>()?;
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let mut probs = LANGUAGES.iter().zip(probs.iter()).collect::<Vec<_>>();
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probs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
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for ((_, language), p) in probs.iter().take(5) {
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println!("{language}: {p}")
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}
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let language = crate::token_id(tokenizer, &format!("<|{}|>", probs[0].0 .0))?;
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Ok(language)
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}
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