Speaker embeddings computation for metavoice. (#1800)

* Speaker embeddings computation for metavoice.

* Compute the speaker embeddings.

candle-transformers/src/models/metavoice.rs

@@ -1,4 +1,4 @@
-use candle::{DType, Error as E, IndexOp, Module, Result, Tensor, D};
+use candle::{DType, Device, Error as E, IndexOp, Module, Result, Tensor, D};
 use candle_nn::{embedding, linear_b, rms_norm, Embedding, Linear, RmsNorm, VarBuilder};
 
 // Equivalent to torch.repeat_interleave
@@ -13,22 +13,41 @@ pub mod speaker_encoder {
 
     #[derive(Debug, Clone, serde::Deserialize)]
     pub struct Config {
-        pub mel_window_step: usize,
-        pub mel_n_channels: usize,
         pub sampling_rate: usize,
         pub partial_n_frames: usize,
         pub model_hidden_size: usize,
         pub model_embedding_size: usize,
         pub model_num_layers: usize,
+        pub mel_window_length: usize,
+        pub mel_window_step: usize,
+        pub mel_n_channels: usize,
+    }
+
+    impl Config {
+        pub fn cfg() -> Self {
+            Self {
+                sampling_rate: 16_000,
+                partial_n_frames: 160,
+                model_hidden_size: 256,
+                model_embedding_size: 256,
+                model_num_layers: 3,
+                mel_window_length: 25,
+                mel_window_step: 10,
+                mel_n_channels: 40,
+            }
+        }
     }
 
     pub struct Model {
         lstms: Vec<candle_nn::LSTM>,
         linear: Linear,
+        cfg: Config,
     }
 
+    type Slice = (usize, usize);
+
     impl Model {
-        pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        pub fn new(cfg: Config, vb: VarBuilder) -> Result<Self> {
             let mut lstms = Vec::with_capacity(cfg.model_num_layers);
             let vb_l = vb.pp("lstm");
             for layer_idx in 0..cfg.model_num_layers {
@@ -50,36 +69,103 @@ pub mod speaker_encoder {
                 true,
                 vb.pp("linear"),
             )?;
-            Ok(Self { lstms, linear })
+            Ok(Self { lstms, linear, cfg })
         }
 
         fn compute_partial_slices(
-            _n_samples: usize,
+            &self,
-            _rate: f64,
+            n_samples: usize,
-            _min_coverage: f64,
+            rate: f64,
-        ) -> Result<(Tensor, Tensor)> {
+            min_coverage: f64,
-            todo!()
+        ) -> (Vec<Slice>, Vec<Slice>) {
+            let c = &self.cfg;
+            // Compute how many frames separate two partial utterances
+            let samples_per_frame = c.sampling_rate * c.mel_window_step / 1000;
+            let n_frames = n_samples / samples_per_frame + 1;
+            let frame_step =
+                (c.sampling_rate as f64 / rate / samples_per_frame as f64).round() as usize;
+            let steps = (n_frames + frame_step).saturating_sub(c.partial_n_frames) + 1;
+            // Compute the slices.
+            let mut wav_slices = vec![];
+            let mut mel_slices = vec![];
+            for i in (0..steps).step_by(frame_step) {
+                let mel_range = (i, i + c.partial_n_frames);
+                let wav_range = (
+                    i * samples_per_frame,
+                    (i + c.partial_n_frames) * samples_per_frame,
+                );
+                mel_slices.push(mel_range);
+                wav_slices.push(wav_range);
+            }
+            // Evaluate whether extra padding is warranted or not.
+            let last_wav_range = match wav_slices.last() {
+                None => return (wav_slices, mel_slices),
+                Some(l) => *l,
+            };
+            let coverage = (n_samples - last_wav_range.0) as f64
+                / (last_wav_range.1 - last_wav_range.0) as f64;
+            if coverage > min_coverage && mel_slices.len() > 1 {
+                mel_slices.pop();
+                wav_slices.pop();
+            }
+            (wav_slices, mel_slices)
         }
 
-        pub fn embed_utterance(&self, wav: &[f32], rate: f64, min_coverage: f64) -> Result<Tensor> {
+        pub fn embed_utterance(
-            let (_wav_slices, _mel_slices) =
+            &self,
-                Self::compute_partial_slices(wav.len(), rate, min_coverage)?;
+            wav: &[f32],
-            todo!()
+            mel_filters: &[f32],
+            rate: f64,
+            min_c: f64,
+            device: &Device,
+        ) -> Result<Tensor> {
+            let (wav_slices, mel_slices) = self.compute_partial_slices(wav.len(), rate, min_c);
+            let max_wave_length = match wav_slices.last() {
+                Some(v) => v.1,
+                None => candle::bail!("empty wav slices"),
+            };
+            let wav = if max_wave_length > wav.len() {
+                let mut wav = wav.to_vec();
+                wav.resize(max_wave_length - wav.len(), 0.0);
+                std::borrow::Cow::Owned(wav)
+            } else {
+                std::borrow::Cow::Borrowed(wav)
+            };
+            let mel = crate::models::whisper::audio::log_mel_spectrogram_(
+                wav.as_ref(),
+                mel_filters,
+                /* fft_size */ self.cfg.mel_window_length,
+                /* fft_step */ self.cfg.mel_window_step,
+                self.cfg.mel_n_channels,
+                false,
+            );
+            let mels = mel_slices
+                .iter()
+                .flat_map(|s| [mel[s.0], mel[s.1]])
+                .collect::<Vec<_>>();
+            let mels = Tensor::from_vec(mels, (mel_slices.len(), 2), device)?;
+            let partial_embeds = self.forward(&mels)?;
+            let raw_embed = partial_embeds.mean(0)?;
+            let norm = raw_embed.sqr()?.sum_all()?.sqrt()?;
+            raw_embed.broadcast_div(&norm)
         }
     }
 
     impl Module for Model {
         fn forward(&self, xs: &Tensor) -> Result<Tensor> {
             use candle_nn::RNN;
+
+            // This is different from the Python transformers version as candle LSTM is batch first.
+            let xs = xs.t()?;
             let mut xs = xs.clone();
-            for lstm in self.lstms.iter() {
+            for layer in self.lstms.iter() {
-                let res = lstm.seq(&xs)?;
+                let states = layer.seq(&xs)?;
-                let res: Vec<_> = res.into_iter().map(|s| s.h().clone()).collect();
+                xs = layer.states_to_tensor(&states)?;
-                xs = Tensor::stack(&res, 1)?;
             }
+            let xs = xs.t()?;
             let embeds_raw = xs.apply(&self.linear)?.relu()?;
-            // TODO: normalize.
+            let norm = embeds_raw.sqr()?.sum_keepdim(1)?.sqrt()?;
-            Ok(embeds_raw)
+            embeds_raw.broadcast_div(&norm)
         }
     }
 }

candle-transformers/src/models/whisper/audio.rs

@@ -167,7 +167,7 @@ fn log_mel_spectrogram_w<T: Float>(
     mel
 }
 
-fn log_mel_spectrogram_<T: Float>(
+pub fn log_mel_spectrogram_<T: Float>(
     samples: &[T],
     filters: &[T],
     fft_size: usize,