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Diffstat (limited to 'makima/src/tts/chatterbox.rs')
| -rw-r--r-- | makima/src/tts/chatterbox.rs | 485 |
1 files changed, 485 insertions, 0 deletions
diff --git a/makima/src/tts/chatterbox.rs b/makima/src/tts/chatterbox.rs new file mode 100644 index 0000000..e26bc06 --- /dev/null +++ b/makima/src/tts/chatterbox.rs @@ -0,0 +1,485 @@ +//! Chatterbox TTS engine — ONNX-based (legacy). +//! +//! This is the existing Chatterbox TTS implementation moved from `tts.rs`, +//! now implementing the `TtsEngine` trait for unified access. + +use std::borrow::Cow; +use std::fs; +use std::path::{Path, PathBuf}; +use std::sync::Mutex; + +use hf_hub::api::sync::Api; +use ndarray::{Array2, Array3, Array4, ArrayD, IxDyn}; +use ort::session::Session; +use ort::value::{DynValue, Value}; +use tokenizers::Tokenizer; + +use crate::audio; + +use super::{ + apply_repetition_penalty, argmax, resample_to_24k, AudioChunk, TtsEngine, TtsError, + SAMPLE_RATE, +}; + +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"; + +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())) +} + +pub struct ChatterboxTTS { + speech_encoder: Mutex<Session>, + embed_tokens: Mutex<Session>, + language_model: Mutex<Session>, + conditional_decoder: Mutex<Session>, + tokenizer: Tokenizer, +} + +// SAFETY: Sessions are behind Mutex, Tokenizer is Send+Sync +unsafe impl Send for ChatterboxTTS {} +unsafe impl Sync for ChatterboxTTS {} + +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: Mutex::new(speech_encoder), + embed_tokens: Mutex::new(embed_tokens), + language_model: Mutex::new(language_model), + conditional_decoder: Mutex::new(conditional_decoder), + tokenizer, + }) + } + + pub fn generate_tts(&self) -> Result<Vec<f32>, TtsError> { + Err(TtsError::VoiceRequired) + } + + pub fn generate_tts_with_voice( + &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( + &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() + }; + + let voice_condition = self.encode_voice(&resampled)?; + + 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(); + + let generated_tokens = + self.generate_speech_tokens(&text_input_ids, &voice_condition.audio_features)?; + + 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); + + let audio_samples = self.decode_speech_tokens( + &final_tokens, + &voice_condition.speaker_embeddings, + &voice_condition.speaker_features, + )?; + + Ok(audio_samples) + } + + fn encode_voice(&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 mut encoder = self + .speech_encoder + .lock() + .map_err(|e| TtsError::Inference(e.to_string()))?; + let outputs = encoder.run(ort::inputs!["audio_values" => audio_tensor])?; + + 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( + &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; + + let mut generate_tokens: Vec<i64> = vec![START_SPEECH_TOKEN]; + + 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 { + let current_input_ids = if first_iteration { + text_input_ids.to_vec() + } else { + 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 mut embed = self + .embed_tokens + .lock() + .map_err(|e| TtsError::Inference(e.to_string()))?; + let embed_outputs = embed.run(ort::inputs![input_ids_tensor])?; + extract_f32_tensor(&embed_outputs[0])? + }; + + 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]; + + 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) + }; + + let (logits, new_kv) = self.run_language_model( + inputs_embeds, + position_ids, + attention_mask, + past_key_values, + )?; + + past_key_values = new_kv; + + 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(&mut current_logits, &generate_tokens, repetition_penalty); + + let next_token = argmax(¤t_logits); + generate_tokens.push(next_token); + + if next_token == STOP_SPEECH_TOKEN { + break; + } + + first_iteration = false; + } + + if generate_tokens.len() > 2 { + Ok(generate_tokens[1..generate_tokens.len() - 1].to_vec()) + } else { + Ok(Vec::new()) + } + } + + fn init_kv_cache(seq_len: usize) -> Vec<Array4<f32>> { + 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); + } + cache + } + + fn run_language_model( + &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(), + )); + + 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 mut lm = self + .language_model + .lock() + .map_err(|e| TtsError::Inference(e.to_string()))?; + let outputs = lm.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( + &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 mut decoder = self + .conditional_decoder + .lock() + .map_err(|e| TtsError::Inference(e.to_string()))?; + let outputs = decoder.run(inputs)?; + + let waveform = extract_f32_tensor(&outputs[0])?; + + Ok(waveform.iter().copied().collect()) + } +} + +#[async_trait::async_trait] +impl TtsEngine for ChatterboxTTS { + async fn generate( + &self, + text: &str, + reference_audio: Option<&[f32]>, + reference_sample_rate: Option<u32>, + ) -> Result<Vec<AudioChunk>, TtsError> { + let samples = match reference_audio { + Some(audio) => { + let sr = reference_sample_rate.unwrap_or(SAMPLE_RATE); + self.generate_tts_with_samples(text, audio, sr)? + } + None => return Err(TtsError::VoiceRequired), + }; + + Ok(vec![AudioChunk { + samples, + sample_rate: SAMPLE_RATE, + is_final: true, + }]) + } + + fn is_ready(&self) -> bool { + true + } +} + +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(()) +} |