Experiment: ChatterBoxTTS

1 files changed, 580 insertions, 0 deletions

diff --git a/makima/src/tts.rs b/makima/src/tts.rs
new file mode 100644
index 0000000..5198938
--- /dev/null
+++ b/makima/src/tts.rs
@@ -0,0 +1,580 @@
+use std::path::{Path, PathBuf};
+use std::fs;
+
+use hf_hub::api::sync::Api;
+use std::borrow::Cow;
+
+use ndarray::{ArrayD, Array2, Array3, Array4, IxDyn};
+use ort::session::Session;
+use ort::value::{Value, DynValue};
+use tokenizers::Tokenizer;
+
+use crate::audio;
+
+pub const SAMPLE_RATE: u32 = 24_000;
+const START_SPEECH_TOKEN: i64 = 6561;
+const STOP_SPEECH_TOKEN: i64 = 6562;
+const SILENCE_TOKEN: i64 = 4299;
+const NUM_LAYERS: usize = 24;
+const NUM_KV_HEADS: usize = 16;
+const HEAD_DIM: usize = 64;
+
+const MODEL_ID: &str = "ResembleAI/chatterbox-turbo-ONNX";
+const DEFAULT_MODEL_DIR: &str = "models/chatterbox-turbo";
+
+#[derive(Debug)]
+pub enum TtsError {
+    ModelLoad(String),
+    Inference(String),
+    Tokenizer(String),
+    Audio(audio::AudioError),
+    Io(std::io::Error),
+    VoiceRequired,
+}
+
+impl std::fmt::Display for TtsError {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        match self {
+            TtsError::ModelLoad(msg) => write!(f, "model load error: {msg}"),
+            TtsError::Inference(msg) => write!(f, "inference error: {msg}"),
+            TtsError::Tokenizer(msg) => write!(f, "tokenizer error: {msg}"),
+            TtsError::Audio(err) => write!(f, "audio error: {err}"),
+            TtsError::Io(err) => write!(f, "io error: {err}"),
+            TtsError::VoiceRequired => write!(f, "voice reference audio is required for chatterbox-turbo"),
+        }
+    }
+}
+
+impl std::error::Error for TtsError {}
+
+impl From<audio::AudioError> for TtsError {
+    fn from(value: audio::AudioError) -> Self {
+        TtsError::Audio(value)
+    }
+}
+
+impl From<std::io::Error> for TtsError {
+    fn from(value: std::io::Error) -> Self {
+        TtsError::Io(value)
+    }
+}
+
+impl From<ort::Error> for TtsError {
+    fn from(value: ort::Error) -> Self {
+        TtsError::ModelLoad(value.to_string())
+    }
+}
+
+pub struct ChatterboxTTS {
+    speech_encoder: Session,
+    embed_tokens: Session,
+    language_model: Session,
+    conditional_decoder: Session,
+    tokenizer: Tokenizer,
+}
+
+struct VoiceCondition {
+    audio_features: ArrayD<f32>,
+    prompt_tokens: ArrayD<i64>,
+    speaker_embeddings: ArrayD<f32>,
+    speaker_features: ArrayD<f32>,
+}
+
+fn extract_f32_tensor(value: &Value) -> Result<ArrayD<f32>, TtsError> {
+    let (shape, data) = value
+        .try_extract_tensor::<f32>()
+        .map_err(|e| TtsError::Inference(e.to_string()))?;
+
+    let dims: Vec<usize> = shape.iter().map(|&d| d as usize).collect();
+    ArrayD::from_shape_vec(IxDyn(&dims), data.to_vec())
+        .map_err(|e| TtsError::Inference(e.to_string()))
+}
+
+fn extract_i64_tensor(value: &Value) -> Result<ArrayD<i64>, TtsError> {
+    let (shape, data) = value
+        .try_extract_tensor::<i64>()
+        .map_err(|e| TtsError::Inference(e.to_string()))?;
+
+    let dims: Vec<usize> = shape.iter().map(|&d| d as usize).collect();
+    ArrayD::from_shape_vec(IxDyn(&dims), data.to_vec())
+        .map_err(|e| TtsError::Inference(e.to_string()))
+}
+
+impl ChatterboxTTS {
+    pub fn from_pretrained(model_dir: Option<&str>) -> Result<Self, TtsError> {
+        let model_path = PathBuf::from(model_dir.unwrap_or(DEFAULT_MODEL_DIR));
+
+        if !model_path.exists() {
+            download_models(&model_path)?;
+        }
+
+        Self::load_from_path(&model_path)
+    }
+
+    pub fn load_from_path(model_dir: &Path) -> Result<Self, TtsError> {
+        let speech_encoder = Session::builder()?
+            .with_intra_threads(4)?
+            .commit_from_file(model_dir.join("speech_encoder.onnx"))?;
+
+        let embed_tokens = Session::builder()?
+            .with_intra_threads(4)?
+            .commit_from_file(model_dir.join("embed_tokens.onnx"))?;
+
+        let language_model = Session::builder()?
+            .with_intra_threads(4)?
+            .commit_from_file(model_dir.join("language_model.onnx"))?;
+
+        let conditional_decoder = Session::builder()?
+            .with_intra_threads(4)?
+            .commit_from_file(model_dir.join("conditional_decoder.onnx"))?;
+
+        let tokenizer_path = model_dir.join("tokenizer.json");
+        let tokenizer = Tokenizer::from_file(&tokenizer_path)
+            .map_err(|e| TtsError::Tokenizer(e.to_string()))?;
+
+        Ok(Self {
+            speech_encoder,
+            embed_tokens,
+            language_model,
+            conditional_decoder,
+            tokenizer,
+        })
+    }
+
+    pub fn generate_tts(&mut self, _text: &str) -> Result<Vec<f32>, TtsError> {
+        // Chatterbox TTS requires voice reference audio
+        Err(TtsError::VoiceRequired)
+    }
+
+    pub fn generate_tts_with_voice(
+        &mut self,
+        text: &str,
+        sample_audio_path: &Path,
+    ) -> Result<Vec<f32>, TtsError> {
+        let audio = audio::to_16k_mono_from_path(sample_audio_path)?;
+        let resampled = resample_to_24k(&audio.samples, audio.sample_rate);
+        self.generate_tts_with_samples(text, &resampled, SAMPLE_RATE)
+    }
+
+    pub fn generate_tts_with_samples(
+        &mut self,
+        text: &str,
+        samples: &[f32],
+        sample_rate: u32,
+    ) -> Result<Vec<f32>, TtsError> {
+        let resampled = if sample_rate != SAMPLE_RATE {
+            resample_to_24k(samples, sample_rate)
+        } else {
+            samples.to_vec()
+        };
+
+        // 1. Encode reference audio
+        let voice_condition = self.encode_voice(&resampled)?;
+
+        // 2. Tokenize text
+        let encoding = self
+            .tokenizer
+            .encode(text, true)
+            .map_err(|e| TtsError::Tokenizer(e.to_string()))?;
+
+        let text_input_ids: Vec<i64> = encoding.get_ids().iter().map(|&id| id as i64).collect();
+
+        // 3. Generate speech tokens
+        let generated_tokens = self.generate_speech_tokens(
+            &text_input_ids,
+            &voice_condition.audio_features,
+        )?;
+
+        // 4. Prepare final speech tokens: prompt_tokens + generated + silence
+        let prompt_tokens: Vec<i64> = voice_condition.prompt_tokens.iter().copied().collect();
+        let silence_tokens = vec![SILENCE_TOKEN; 3];
+
+        let mut final_tokens = Vec::with_capacity(
+            prompt_tokens.len() + generated_tokens.len() + silence_tokens.len()
+        );
+        final_tokens.extend_from_slice(&prompt_tokens);
+        final_tokens.extend_from_slice(&generated_tokens);
+        final_tokens.extend_from_slice(&silence_tokens);
+
+        // 5. Decode to audio
+        let audio_samples = self.decode_speech_tokens(
+            &final_tokens,
+            &voice_condition.speaker_embeddings,
+            &voice_condition.speaker_features,
+        )?;
+
+        Ok(audio_samples)
+    }
+
+    fn encode_voice(&mut self, samples: &[f32]) -> Result<VoiceCondition, TtsError> {
+        let audio_arr = Array2::from_shape_vec((1, samples.len()), samples.to_vec())
+            .map_err(|e| TtsError::Inference(e.to_string()))?;
+
+        let audio_tensor = Value::from_array(audio_arr)?;
+
+        let outputs = self.speech_encoder.run(ort::inputs!["audio_values" => audio_tensor])?;
+
+        // Order: audio_features, audio_tokens (prompt_token), speaker_embeddings, speaker_features
+        let audio_features = extract_f32_tensor(&outputs[0])?;
+        let prompt_tokens = extract_i64_tensor(&outputs[1])?;
+        let speaker_embeddings = extract_f32_tensor(&outputs[2])?;
+        let speaker_features = extract_f32_tensor(&outputs[3])?;
+
+        Ok(VoiceCondition {
+            audio_features,
+            prompt_tokens,
+            speaker_embeddings,
+            speaker_features,
+        })
+    }
+
+    fn generate_speech_tokens(
+        &mut self,
+        text_input_ids: &[i64],
+        audio_features: &ArrayD<f32>,
+    ) -> Result<Vec<i64>, TtsError> {
+        let max_new_tokens: usize = 1024;
+        let repetition_penalty: f32 = 1.2;
+
+        // Start with START_SPEECH_TOKEN
+        let mut generate_tokens: Vec<i64> = vec![START_SPEECH_TOKEN];
+
+        // Initialize empty KV cache (seq_len = 0)
+        let mut past_key_values = self.init_kv_cache(0)?;
+
+        let mut first_iteration = true;
+        let mut total_seq_len: usize = 0;
+
+        for _ in 0..max_new_tokens {
+            // Get embeddings for current input_ids
+            let current_input_ids = if first_iteration {
+                // First iteration: use text input_ids
+                text_input_ids.to_vec()
+            } else {
+                // Subsequent iterations: use last generated token
+                vec![*generate_tokens.last().unwrap()]
+            };
+
+            let input_ids_arr = Array2::from_shape_vec(
+                (1, current_input_ids.len()),
+                current_input_ids
+            ).map_err(|e| TtsError::Inference(e.to_string()))?;
+
+            let input_ids_tensor = Value::from_array(input_ids_arr)?;
+
+            let inputs_embeds = {
+                let embed_outputs = self.embed_tokens.run(ort::inputs![input_ids_tensor])?;
+                extract_f32_tensor(&embed_outputs[0])?
+            };
+
+            // On first iteration, concatenate audio features with text embeddings
+            let inputs_embeds = if first_iteration {
+                let audio_feat_3d = audio_features.view()
+                    .into_dimensionality::<ndarray::Ix3>()
+                    .map_err(|e| TtsError::Inference(e.to_string()))?;
+                let text_emb_3d = inputs_embeds.view()
+                    .into_dimensionality::<ndarray::Ix3>()
+                    .map_err(|e| TtsError::Inference(e.to_string()))?;
+
+                ndarray::concatenate(ndarray::Axis(1), &[audio_feat_3d, text_emb_3d])
+                    .map_err(|e| TtsError::Inference(e.to_string()))?
+            } else {
+                inputs_embeds.view()
+                    .into_dimensionality::<ndarray::Ix3>()
+                    .map_err(|e| TtsError::Inference(e.to_string()))?
+                    .to_owned()
+            };
+
+            let seq_len = inputs_embeds.shape()[1];
+
+            // Set up attention mask and position ids
+            let (attention_mask, position_ids) = if first_iteration {
+                total_seq_len = seq_len;
+                let attention_mask: Array2<i64> = Array2::ones((1, seq_len));
+                let position_ids = Array2::from_shape_fn((1, seq_len), |(_, j)| j as i64);
+                (attention_mask, position_ids)
+            } else {
+                total_seq_len += 1;
+                let attention_mask: Array2<i64> = Array2::ones((1, total_seq_len));
+                let position_ids = Array2::from_shape_vec(
+                    (1, 1),
+                    vec![(total_seq_len - 1) as i64]
+                ).map_err(|e| TtsError::Inference(e.to_string()))?;
+                (attention_mask, position_ids)
+            };
+
+            // Run language model
+            let (logits, new_kv) = self.run_language_model(
+                inputs_embeds,
+                position_ids,
+                attention_mask,
+                past_key_values,
+            )?;
+
+            past_key_values = new_kv;
+
+            // Get last logits
+            let logits_3d = logits.view().into_dimensionality::<ndarray::Ix3>()
+                .map_err(|e| TtsError::Inference(e.to_string()))?;
+            let last_idx = logits_3d.shape()[1] - 1;
+
+            let mut current_logits: Vec<f32> = logits_3d
+                .slice(ndarray::s![0, last_idx, ..])
+                .iter()
+                .copied()
+                .collect();
+
+            // Apply repetition penalty
+            apply_repetition_penalty(&mut current_logits, &generate_tokens, repetition_penalty);
+
+            // Get next token
+            let next_token = argmax(&current_logits);
+
+            generate_tokens.push(next_token);
+
+            if next_token == STOP_SPEECH_TOKEN {
+                break;
+            }
+
+            first_iteration = false;
+        }
+
+        // Return tokens without START and STOP tokens: [1:-1]
+        if generate_tokens.len() > 2 {
+            Ok(generate_tokens[1..generate_tokens.len()-1].to_vec())
+        } else {
+            Ok(Vec::new())
+        }
+    }
+
+    fn init_kv_cache(&self, seq_len: usize) -> Result<Vec<Array4<f32>>, TtsError> {
+        let mut cache = Vec::with_capacity(NUM_LAYERS * 2);
+        for _ in 0..NUM_LAYERS {
+            let key = Array4::<f32>::zeros((1, NUM_KV_HEADS, seq_len, HEAD_DIM));
+            let value = Array4::<f32>::zeros((1, NUM_KV_HEADS, seq_len, HEAD_DIM));
+            cache.push(key);
+            cache.push(value);
+        }
+        Ok(cache)
+    }
+
+    fn run_language_model(
+        &mut self,
+        inputs_embeds: Array3<f32>,
+        position_ids: Array2<i64>,
+        attention_mask: Array2<i64>,
+        past_key_values: Vec<Array4<f32>>,
+    ) -> Result<(ArrayD<f32>, Vec<Array4<f32>>), TtsError> {
+        let mut inputs: Vec<(Cow<str>, DynValue)> = Vec::new();
+
+        inputs.push((Cow::from("inputs_embeds"), Value::from_array(inputs_embeds)?.into_dyn()));
+        inputs.push((Cow::from("position_ids"), Value::from_array(position_ids)?.into_dyn()));
+        inputs.push((Cow::from("attention_mask"), Value::from_array(attention_mask)?.into_dyn()));
+
+        // Add KV cache inputs
+        for layer_idx in 0..NUM_LAYERS {
+            let key_name = format!("past_key_values.{}.key", layer_idx);
+            let value_name = format!("past_key_values.{}.value", layer_idx);
+
+            let key_tensor = Value::from_array(past_key_values[layer_idx * 2].clone())?.into_dyn();
+            let value_tensor = Value::from_array(past_key_values[layer_idx * 2 + 1].clone())?.into_dyn();
+
+            inputs.push((Cow::from(key_name), key_tensor));
+            inputs.push((Cow::from(value_name), value_tensor));
+        }
+
+        let outputs = self.language_model.run(inputs)?;
+
+        let logits = extract_f32_tensor(&outputs[0])?;
+
+        let mut new_kv = Vec::with_capacity(NUM_LAYERS * 2);
+        for layer_idx in 0..NUM_LAYERS {
+            let key_idx = 1 + layer_idx * 2;
+            let value_idx = 2 + layer_idx * 2;
+
+            let key_arr = extract_f32_tensor(&outputs[key_idx])?;
+            let value_arr = extract_f32_tensor(&outputs[value_idx])?;
+
+            let key_4d = key_arr.into_dimensionality::<ndarray::Ix4>()
+                .map_err(|e| TtsError::Inference(e.to_string()))?;
+            let value_4d = value_arr.into_dimensionality::<ndarray::Ix4>()
+                .map_err(|e| TtsError::Inference(e.to_string()))?;
+
+            new_kv.push(key_4d.to_owned());
+            new_kv.push(value_4d.to_owned());
+        }
+
+        Ok((logits, new_kv))
+    }
+
+    fn decode_speech_tokens(
+        &mut self,
+        speech_tokens: &[i64],
+        speaker_embeddings: &ArrayD<f32>,
+        speaker_features: &ArrayD<f32>,
+    ) -> Result<Vec<f32>, TtsError> {
+        if speech_tokens.is_empty() {
+            return Ok(Vec::new());
+        }
+
+        let tokens_arr = Array2::from_shape_vec((1, speech_tokens.len()), speech_tokens.to_vec())
+            .map_err(|e| TtsError::Inference(e.to_string()))?;
+
+        let mut inputs: Vec<(Cow<str>, DynValue)> = Vec::new();
+        inputs.push((Cow::from("speech_tokens"), Value::from_array(tokens_arr)?.into_dyn()));
+        inputs.push((Cow::from("speaker_embeddings"), Value::from_array(speaker_embeddings.clone())?.into_dyn()));
+        inputs.push((Cow::from("speaker_features"), Value::from_array(speaker_features.clone())?.into_dyn()));
+
+        let outputs = self.conditional_decoder.run(inputs)?;
+
+        let waveform = extract_f32_tensor(&outputs[0])?;
+
+        Ok(waveform.iter().copied().collect())
+    }
+}
+
+fn download_models(target_dir: &Path) -> Result<(), TtsError> {
+    fs::create_dir_all(target_dir)?;
+
+    let api = Api::new().map_err(|e| TtsError::ModelLoad(e.to_string()))?;
+    let repo = api.model(MODEL_ID.to_string());
+
+    let model_files = [
+        "onnx/speech_encoder.onnx",
+        "onnx/speech_encoder.onnx_data",
+        "onnx/embed_tokens.onnx",
+        "onnx/embed_tokens.onnx_data",
+        "onnx/language_model.onnx",
+        "onnx/language_model.onnx_data",
+        "onnx/conditional_decoder.onnx",
+        "onnx/conditional_decoder.onnx_data",
+        "tokenizer.json",
+    ];
+
+    for file in &model_files {
+        println!("Downloading {}...", file);
+        let downloaded_path = repo.get(file).map_err(|e| TtsError::ModelLoad(e.to_string()))?;
+
+        let filename = Path::new(file).file_name().unwrap();
+        let target_path = target_dir.join(filename);
+
+        if !target_path.exists() {
+            fs::copy(&downloaded_path, &target_path)?;
+        }
+    }
+
+    println!("Models downloaded to {:?}", target_dir);
+    Ok(())
+}
+
+fn resample_to_24k(samples: &[f32], input_rate: u32) -> Vec<f32> {
+    if input_rate == SAMPLE_RATE {
+        return samples.to_vec();
+    }
+    if samples.is_empty() {
+        return Vec::new();
+    }
+
+    let ratio = input_rate as f64 / SAMPLE_RATE as f64;
+    let output_len = ((samples.len() as f64) / ratio).ceil() as usize;
+
+    let mut output = Vec::with_capacity(output_len);
+    for i in 0..output_len {
+        let src_idx = (i as f64 * ratio) as usize;
+        let sample = samples.get(src_idx).copied().unwrap_or(0.0);
+        output.push(sample);
+    }
+
+    output
+}
+
+fn apply_repetition_penalty(logits: &mut [f32], generated: &[i64], penalty: f32) {
+    for &token in generated {
+        if (token as usize) < logits.len() {
+            let score = logits[token as usize];
+            // Note: opposite of standard - if score < 0, multiply; if > 0, divide
+            logits[token as usize] = if score < 0.0 {
+                score * penalty
+            } else {
+                score / penalty
+            };
+        }
+    }
+}
+
+fn argmax(logits: &[f32]) -> i64 {
+    logits
+        .iter()
+        .enumerate()
+        .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
+        .map(|(idx, _)| idx as i64)
+        .unwrap_or(0)
+}
+
+pub fn save_wav(samples: &[f32], path: &Path) -> Result<(), TtsError> {
+    let mut file = fs::File::create(path)?;
+    write_wav(&mut file, samples, SAMPLE_RATE)?;
+    Ok(())
+}
+
+fn write_wav<W: std::io::Write>(writer: &mut W, samples: &[f32], sample_rate: u32) -> Result<(), std::io::Error> {
+    let num_samples = samples.len() as u32;
+    let num_channels: u16 = 1;
+    let bits_per_sample: u16 = 16;
+    let byte_rate = sample_rate * num_channels as u32 * bits_per_sample as u32 / 8;
+    let block_align = num_channels * bits_per_sample / 8;
+    let data_size = num_samples * num_channels as u32 * bits_per_sample as u32 / 8;
+    let file_size = 36 + data_size;
+
+    writer.write_all(b"RIFF")?;
+    writer.write_all(&file_size.to_le_bytes())?;
+    writer.write_all(b"WAVE")?;
+
+    writer.write_all(b"fmt ")?;
+    writer.write_all(&16u32.to_le_bytes())?;
+    writer.write_all(&1u16.to_le_bytes())?;
+    writer.write_all(&num_channels.to_le_bytes())?;
+    writer.write_all(&sample_rate.to_le_bytes())?;
+    writer.write_all(&byte_rate.to_le_bytes())?;
+    writer.write_all(&block_align.to_le_bytes())?;
+    writer.write_all(&bits_per_sample.to_le_bytes())?;
+
+    writer.write_all(b"data")?;
+    writer.write_all(&data_size.to_le_bytes())?;
+
+    for &sample in samples {
+        let clamped = sample.clamp(-1.0, 1.0);
+        let int_sample = (clamped * 32767.0) as i16;
+        writer.write_all(&int_sample.to_le_bytes())?;
+    }
+
+    Ok(())
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_argmax() {
+        let logits = vec![0.1, 0.5, 0.3, 0.8, 0.2];
+        assert_eq!(argmax(&logits), 3);
+    }
+
+    #[test]
+    fn test_resample_same_rate() {
+        let samples = vec![0.1, 0.2, 0.3];
+        let resampled = resample_to_24k(&samples, SAMPLE_RATE);
+        assert_eq!(resampled, samples);
+    }
+
+    #[test]
+    fn test_repetition_penalty() {
+        let mut logits = vec![1.0, 2.0, 3.0, 4.0];
+        let generated = vec![1, 3];
+        apply_repetition_penalty(&mut logits, &generated, 1.2);
+        // score > 0 -> divide
+        assert!((logits[1] - 2.0 / 1.2).abs() < 1e-6);
+        assert!((logits[3] - 4.0 / 1.2).abs() < 1e-6);
+    }
+}