diff options
| -rw-r--r-- | makima/Cargo.lock | 1388 | ||||
| -rw-r--r-- | makima/Cargo.toml | 6 | ||||
| -rw-r--r-- | makima/src/audio.rs | 375 | ||||
| -rw-r--r-- | makima/src/listen.rs | 97 | ||||
| -rw-r--r-- | makima/src/main.rs | 38 | ||||
| -rw-r--r-- | makima/src/tts.rs | 580 |
6 files changed, 2427 insertions, 57 deletions
diff --git a/makima/Cargo.lock b/makima/Cargo.lock index 1fcd019..f3fea81 100644 --- a/makima/Cargo.lock +++ b/makima/Cargo.lock @@ -9,6 +9,20 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "320119579fcad9c21884f5c4861d16174d0e06250625266f50fe6898340abefa" [[package]] +name = "ahash" +version = "0.8.12" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "5a15f179cd60c4584b8a8c596927aadc462e27f2ca70c04e0071964a73ba7a75" +dependencies = [ + "cfg-if", + "getrandom 0.3.4", + "once_cell", + "serde", + "version_check", + "zerocopy", +] + +[[package]] name = "aho-corasick" version = "1.1.4" source = "registry+https://github.com/rust-lang/crates.io-index" @@ -18,6 +32,18 @@ dependencies = [ ] [[package]] +name = "arrayvec" +version = "0.7.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7c02d123df017efcdfbd739ef81735b36c5ba83ec3c59c80a9d7ecc718f92e50" + +[[package]] +name = "atomic-waker" +version = "1.1.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1505bd5d3d116872e7271a6d4e16d81d0c8570876c8de68093a09ac269d8aac0" + +[[package]] name = "autocfg" version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" @@ -43,6 +69,12 @@ checksum = "0e050f626429857a27ddccb31e0aca21356bfa709c04041aefddac081a8f068a" [[package]] name = "bitflags" +version = "1.3.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "bef38d45163c2f1dde094a7dfd33ccf595c92905c8f8f4fdc18d06fb1037718a" + +[[package]] +name = "bitflags" version = "2.10.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "812e12b5285cc515a9c72a5c1d3b6d46a19dac5acfef5265968c166106e31dd3" @@ -63,6 +95,12 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "46c5e41b57b8bba42a04676d81cb89e9ee8e859a1a66f80a5a72e1cb76b34d43" [[package]] +name = "bytemuck" +version = "1.24.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1fbdf580320f38b612e485521afda1ee26d10cc9884efaaa750d383e13e3c5f4" + +[[package]] name = "byteorder" version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" @@ -75,6 +113,15 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b35204fbdc0b3f4446b89fc1ac2cf84a8a68971995d0bf2e925ec7cd960f9cb3" [[package]] +name = "castaway" +version = "0.2.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "dec551ab6e7578819132c713a93c022a05d60159dc86e7a7050223577484c55a" +dependencies = [ + "rustversion", +] + +[[package]] name = "cc" version = "1.2.49" source = "registry+https://github.com/rust-lang/crates.io-index" @@ -91,6 +138,21 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "9330f8b2ff13f34540b44e946ef35111825727b38d33286ef986142615121801" [[package]] +name = "compact_str" +version = "0.9.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3fdb1325a1cece981e8a296ab8f0f9b63ae357bd0784a9faaf548cc7b480707a" +dependencies = [ + "castaway", + "cfg-if", + "itoa", + "rustversion", + "ryu", + "serde", + "static_assertions", +] + +[[package]] name = "console" version = "0.15.11" source = "registry+https://github.com/rust-lang/crates.io-index" @@ -208,6 +270,15 @@ dependencies = [ ] [[package]] +name = "dary_heap" +version = "0.3.8" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "06d2e3287df1c007e74221c49ca10a95d557349e54b3a75dc2fb14712c751f04" +dependencies = [ + "serde", +] + +[[package]] name = "der" version = "0.7.10" source = "registry+https://github.com/rust-lang/crates.io-index" @@ -259,6 +330,38 @@ dependencies = [ ] [[package]] +name = "dirs" +version = "6.0.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c3e8aa94d75141228480295a7d0e7feb620b1a5ad9f12bc40be62411e38cce4e" +dependencies = [ + "dirs-sys", +] + +[[package]] +name = "dirs-sys" +version = "0.5.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e01a3366d27ee9890022452ee61b2b63a67e6f13f58900b651ff5665f0bb1fab" +dependencies = [ + "libc", + "option-ext", + "redox_users", + "windows-sys 0.61.2", +] + +[[package]] +name = "displaydoc" +version = "0.2.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "97369cbbc041bc366949bc74d34658d6cda5621039731c6310521892a3a20ae0" +dependencies = [ + "proc-macro2", + "quote", + "syn", +] + +[[package]] name = "either" version = "1.15.0" source = "registry+https://github.com/rust-lang/crates.io-index" @@ -271,6 +374,21 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "34aa73646ffb006b8f5147f3dc182bd4bcb190227ce861fc4a4844bf8e3cb2c0" [[package]] +name = "encoding_rs" +version = "0.8.35" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "75030f3c4f45dafd7586dd6780965a8c7e8e285a5ecb86713e63a79c5b2766f3" +dependencies = [ + "cfg-if", +] + +[[package]] +name = "equivalent" +version = "1.0.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "877a4ace8713b0bcf2a4e7eec82529c029f1d0619886d18145fea96c3ffe5c0f" + +[[package]] name = "errno" version = "0.3.14" source = "registry+https://github.com/rust-lang/crates.io-index" @@ -290,6 +408,12 @@ dependencies = [ ] [[package]] +name = "extended" +version = "0.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "af9673d8203fcb076b19dfd17e38b3d4ae9f44959416ea532ce72415a6020365" + +[[package]] name = "eyre" version = "0.6.12" source = "registry+https://github.com/rust-lang/crates.io-index" @@ -355,6 +479,104 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "00b0228411908ca8685dba7fc2cdd70ec9990a6e753e89b6ac91a84c40fbaf4b" [[package]] +name = "form_urlencoded" +version = "1.2.2" +source = 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dependencies = [ "ppv-lite86", - "rand_core", + "rand_core 0.6.4", +] + +[[package]] +name = "rand_chacha" +version = "0.9.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d3022b5f1df60f26e1ffddd6c66e8aa15de382ae63b3a0c1bfc0e4d3e3f325cb" +dependencies = [ + "ppv-lite86", + "rand_core 0.9.3", ] [[package]] @@ -890,6 +1485,15 @@ dependencies = [ ] [[package]] +name = "rand_core" +version = "0.9.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "99d9a13982dcf210057a8a78572b2217b667c3beacbf3a0d8b454f6f82837d38" +dependencies = [ + "getrandom 0.3.4", +] + +[[package]] name = "rawpointer" version = "0.2.1" source = "registry+https://github.com/rust-lang/crates.io-index" @@ -917,6 +1521,17 @@ dependencies = [ ] [[package]] +name = "rayon-cond" +version = "0.4.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "2964d0cf57a3e7a06e8183d14a8b527195c706b7983549cd5462d5aa3747438f" +dependencies = [ + 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"b97154e67e32c85465826e8bcc1c59429aaaf107c1e4a9e53c8d8ccd5eff88d0" + +[[package]] +name = "zerotrie" +version = "0.2.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "2a59c17a5562d507e4b54960e8569ebee33bee890c70aa3fe7b97e85a9fd7851" +dependencies = [ + "displaydoc", + "yoke", + "zerofrom", +] + +[[package]] +name = "zerovec" +version = "0.11.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6c28719294829477f525be0186d13efa9a3c602f7ec202ca9e353d310fb9a002" +dependencies = [ + "yoke", + "zerofrom", + "zerovec-derive", +] + +[[package]] +name = "zerovec-derive" +version = "0.11.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "eadce39539ca5cb3985590102671f2567e659fca9666581ad3411d59207951f3" +dependencies = [ + "proc-macro2", + "quote", + "syn", +] diff --git a/makima/Cargo.toml b/makima/Cargo.toml index 3f3abb7..df7384f 100644 --- a/makima/Cargo.toml +++ b/makima/Cargo.toml @@ -5,4 +5,8 @@ edition = "2024" [dependencies] parakeet-rs = { version = "0.2.5", features = ["sortformer"] } -hound = "3.5" +symphonia = { version = "0.5", features = ["mp3", "aac", "flac", "ogg", "vorbis", "wav", "pcm"] } +ort = "2.0.0-rc.10" +tokenizers = "0.21" +hf-hub = "0.4" +ndarray = "0.16" diff --git a/makima/src/audio.rs b/makima/src/audio.rs new file mode 100644 index 0000000..acfe7ce --- /dev/null +++ b/makima/src/audio.rs @@ -0,0 +1,375 @@ +use std::fs::File; +use std::io::{self, Read, Seek}; +use std::path::Path; + +use symphonia::core::audio::{AudioBufferRef, Signal}; +use symphonia::core::codecs::{DecoderOptions, CODEC_TYPE_NULL}; +use symphonia::core::errors::Error as SymphoniaError; +use symphonia::core::formats::FormatOptions; +use symphonia::core::io::{MediaSourceStream, ReadOnlySource}; +use symphonia::core::meta::MetadataOptions; +use symphonia::core::probe::Hint; + +pub const TARGET_SAMPLE_RATE: u32 = 16_000; +pub const TARGET_CHANNELS: u16 = 1; + +#[derive(Debug, Clone)] +pub struct PcmAudio { + pub samples: Vec<f32>, + pub sample_rate: u32, + pub channels: u16, +} + +#[derive(Debug)] +pub enum AudioError { + Io(io::Error), + Decode(String), + UnsupportedFormat, + NoAudioTrack, +} + +impl std::fmt::Display for AudioError { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + match self { + AudioError::Io(err) => write!(f, "io error: {err}"), + AudioError::Decode(err) => write!(f, "decode error: {err}"), + AudioError::UnsupportedFormat => write!(f, "unsupported audio format"), + AudioError::NoAudioTrack => write!(f, "no audio track found"), + } + } +} + +impl std::error::Error for AudioError {} + +impl From<io::Error> for AudioError { + fn from(value: io::Error) -> Self { + AudioError::Io(value) + } +} + +impl From<SymphoniaError> for AudioError { + fn from(value: SymphoniaError) -> Self { + match value { + SymphoniaError::IoError(e) => AudioError::Io(e), + SymphoniaError::Unsupported(_) => AudioError::UnsupportedFormat, + other => AudioError::Decode(other.to_string()), + } + } +} + +pub fn to_16k_mono_from_path(path: impl AsRef<Path>) -> Result<PcmAudio, AudioError> { + let path = path.as_ref(); + let file = File::open(path)?; + + let mut hint = Hint::new(); + if let Some(ext) = path.extension().and_then(|e| e.to_str()) { + hint.with_extension(ext); + } + + decode_to_16k_mono(file, hint) +} + +pub fn to_16k_mono_from_reader<R: Read + Seek + Send + Sync + 'static>( + reader: R, +) -> Result<PcmAudio, AudioError> { + decode_to_16k_mono(reader, Hint::new()) +} + +fn decode_to_16k_mono<R: Read + Seek + Send + Sync + 'static>( + reader: R, + hint: Hint, +) -> Result<PcmAudio, AudioError> { + let source = MediaSourceStream::new(Box::new(ReadOnlySource::new(reader)), Default::default()); + + let format_opts = FormatOptions::default(); + let metadata_opts = MetadataOptions::default(); + + let probed = symphonia::default::get_probe().format(&hint, source, &format_opts, &metadata_opts)?; + let mut format = probed.format; + + let track = format + .tracks() + .iter() + .find(|t| t.codec_params.codec != CODEC_TYPE_NULL) + .ok_or(AudioError::NoAudioTrack)?; + + let track_id = track.id; + let codec_params = track.codec_params.clone(); + + let sample_rate = codec_params.sample_rate.ok_or(AudioError::Decode( + "unknown sample rate".to_string(), + ))?; + let channels = codec_params + .channels + .map(|c| c.count() as u16) + .unwrap_or(1); + + let decoder_opts = DecoderOptions::default(); + let mut decoder = symphonia::default::get_codecs().make(&codec_params, &decoder_opts)?; + + let mut interleaved: Vec<f32> = Vec::new(); + + loop { + let packet = match format.next_packet() { + Ok(p) => p, + Err(SymphoniaError::IoError(ref e)) if e.kind() == io::ErrorKind::UnexpectedEof => break, + Err(SymphoniaError::ResetRequired) => { + decoder.reset(); + continue; + } + Err(e) => return Err(e.into()), + }; + + if packet.track_id() != track_id { + continue; + } + + let decoded = match decoder.decode(&packet) { + Ok(d) => d, + Err(SymphoniaError::DecodeError(_)) => continue, + Err(e) => return Err(e.into()), + }; + + append_samples(&decoded, &mut interleaved); + } + + let mono = mixdown_to_mono(&interleaved, channels); + let samples = resample_sinc(&mono, sample_rate, TARGET_SAMPLE_RATE); + + Ok(PcmAudio { + samples, + sample_rate: TARGET_SAMPLE_RATE, + channels: TARGET_CHANNELS, + }) +} + +fn append_samples(buffer: &AudioBufferRef, out: &mut Vec<f32>) { + match buffer { + AudioBufferRef::U8(buf) => { + for frame in 0..buf.frames() { + for plane in buf.planes().planes() { + out.push((plane[frame] as f32 - 128.0) / 128.0); + } + } + } + AudioBufferRef::U16(buf) => { + for frame in 0..buf.frames() { + for plane in buf.planes().planes() { + out.push((plane[frame] as f32 - 32768.0) / 32768.0); + } + } + } + AudioBufferRef::U24(buf) => { + for frame in 0..buf.frames() { + for plane in buf.planes().planes() { + out.push((plane[frame].inner() as f32 - 8388608.0) / 8388608.0); + } + } + } + AudioBufferRef::U32(buf) => { + for frame in 0..buf.frames() { + for plane in buf.planes().planes() { + out.push((plane[frame] as f64 - 2147483648.0) as f32 / 2147483648.0); + } + } + } + AudioBufferRef::S8(buf) => { + for frame in 0..buf.frames() { + for plane in buf.planes().planes() { + out.push(plane[frame] as f32 / 128.0); + } + } + } + AudioBufferRef::S16(buf) => { + for frame in 0..buf.frames() { + for plane in buf.planes().planes() { + out.push(plane[frame] as f32 / 32768.0); + } + } + } + AudioBufferRef::S24(buf) => { + for frame in 0..buf.frames() { + for plane in buf.planes().planes() { + out.push(plane[frame].inner() as f32 / 8388608.0); + } + } + } + AudioBufferRef::S32(buf) => { + for frame in 0..buf.frames() { + for plane in buf.planes().planes() { + out.push(plane[frame] as f32 / 2147483648.0); + } + } + } + AudioBufferRef::F32(buf) => { + for frame in 0..buf.frames() { + for plane in buf.planes().planes() { + out.push(plane[frame]); + } + } + } + AudioBufferRef::F64(buf) => { + for frame in 0..buf.frames() { + for plane in buf.planes().planes() { + out.push(plane[frame] as f32); + } + } + } + } +} + +fn mixdown_to_mono(interleaved: &[f32], channels: u16) -> Vec<f32> { + if channels <= 1 { + return interleaved.to_vec(); + } + + let channels = channels as usize; + let frames = interleaved.len() / channels; + + let mut mono = Vec::with_capacity(frames); + for frame in 0..frames { + let base = frame * channels; + let mut acc = 0.0f32; + for c in 0..channels { + acc += interleaved[base + c]; + } + mono.push(acc / channels as f32); + } + + mono +} + +fn resample_sinc(input: &[f32], input_rate: u32, output_rate: u32) -> Vec<f32> { + if input_rate == output_rate { + return input.to_vec(); + } + if input.is_empty() { + return Vec::new(); + } + + let ratio = input_rate as f64 / output_rate as f64; + let output_len = ((input.len() as f64) / ratio).ceil() as usize; + + let cutoff = (output_rate as f64 / input_rate as f64).min(1.0); + + let radius: i32 = 32; + let radius_f = radius as f64; + let pi = std::f64::consts::PI; + + let mut output = Vec::with_capacity(output_len); + for n in 0..output_len { + let t = n as f64 * ratio; + let center = t.floor() as i32; + let frac = t - (center as f64); + + let mut acc = 0.0f64; + let mut norm = 0.0f64; + + for k in -radius..=radius { + let idx = center + k; + if idx < 0 || (idx as usize) >= input.len() { + continue; + } + + let x = (k as f64) - frac; + let d = x.abs(); + if d > radius_f { + continue; + } + + let window = 0.5 * (1.0 + (pi * d / radius_f).cos()); + + let z = x * cutoff; + let sinc = if z == 0.0 { + 1.0 + } else { + let pz = pi * z; + pz.sin() / pz + }; + + let weight = cutoff * sinc * window; + acc += input[idx as usize] as f64 * weight; + norm += weight; + } + + let y = if norm == 0.0 { 0.0 } else { acc / norm }; + output.push(y as f32); + } + + output +} + +#[cfg(test)] +mod tests { + use super::*; + use std::io::Cursor; + + fn create_wav_buffer(sample_rate: u32, channels: u16, samples: &[i16]) -> Vec<u8> { + let mut buf = Vec::new(); + let data_size = (samples.len() * 2) as u32; + let file_size = 36 + data_size; + + buf.extend_from_slice(b"RIFF"); + buf.extend_from_slice(&file_size.to_le_bytes()); + buf.extend_from_slice(b"WAVE"); + + buf.extend_from_slice(b"fmt "); + buf.extend_from_slice(&16u32.to_le_bytes()); + buf.extend_from_slice(&1u16.to_le_bytes()); + buf.extend_from_slice(&channels.to_le_bytes()); + buf.extend_from_slice(&sample_rate.to_le_bytes()); + let byte_rate = sample_rate * channels as u32 * 2; + buf.extend_from_slice(&byte_rate.to_le_bytes()); + let block_align = channels * 2; + buf.extend_from_slice(&block_align.to_le_bytes()); + buf.extend_from_slice(&16u16.to_le_bytes()); + + buf.extend_from_slice(b"data"); + buf.extend_from_slice(&data_size.to_le_bytes()); + for &s in samples { + buf.extend_from_slice(&s.to_le_bytes()); + } + + buf + } + + #[test] + fn converts_stereo_to_mono() { + let mut samples = Vec::new(); + for _ in 0..(TARGET_SAMPLE_RATE / 10) { + samples.push(10_000i16); + samples.push(0i16); + } + + let wav = create_wav_buffer(TARGET_SAMPLE_RATE, 2, &samples); + let cursor = Cursor::new(wav); + + let normalized = to_16k_mono_from_reader(cursor).unwrap(); + + assert_eq!(normalized.sample_rate, TARGET_SAMPLE_RATE); + assert_eq!(normalized.channels, TARGET_CHANNELS); + let mean = + normalized.samples.iter().copied().sum::<f32>() / normalized.samples.len() as f32; + let expected = (10_000.0 / 32768.0) / 2.0; + assert!((mean - expected).abs() < 1e-3); + } + + #[test] + fn resamples_to_16k() { + let samples: Vec<i16> = vec![0; 48_000]; + let wav = create_wav_buffer(48_000, 1, &samples); + let cursor = Cursor::new(wav); + + let normalized = to_16k_mono_from_reader(cursor).unwrap(); + + assert_eq!(normalized.sample_rate, TARGET_SAMPLE_RATE); + assert_eq!(normalized.channels, TARGET_CHANNELS); + assert_eq!(normalized.samples.len(), TARGET_SAMPLE_RATE as usize); + let max_abs = normalized + .samples + .iter() + .copied() + .fold(0.0f32, |m, v| m.max(v.abs())); + assert!(max_abs <= 1e-6); + } +} diff --git a/makima/src/listen.rs b/makima/src/listen.rs index a0f4246..cd0a394 100644 --- a/makima/src/listen.rs +++ b/makima/src/listen.rs @@ -1,11 +1,12 @@ use std::cmp::Ordering; use std::path::Path; -use hound::WavReader; use parakeet_rs::sortformer::{DiarizationConfig, Sortformer, SpeakerSegment}; use parakeet_rs::{ParakeetTDT, TimedToken, TimestampMode}; -const SAMPLE_RATE: u32 = 16_000; +use crate::audio; + +const STREAM_CHUNK_MS: u32 = 5_000; pub struct DialogueSegment { pub speaker: String, @@ -15,57 +16,54 @@ pub struct DialogueSegment { } pub(crate) fn listen() -> Result<Vec<DialogueSegment>, Box<dyn std::error::Error>> { - let audio_path = Path::new("audio.wav"); + let audio_path = Path::new("audio-ftc.mp3"); - let (audio, sample_rate, channels) = load_audio(audio_path)?; + let normalized = audio::to_16k_mono_from_path(audio_path)?; let mut parakeet = ParakeetTDT::from_pretrained("models/parakeet-tdt-0.6b-v3", None)?; - let transcription = parakeet.transcribe_samples( - audio.clone(), - sample_rate, - channels, - Some(TimestampMode::Sentences), - )?; - let mut sortformer = Sortformer::with_config( "models/diarization/diar_streaming_sortformer_4spk-v2.onnx", None, DiarizationConfig::callhome(), )?; - let diarization_segments = sortformer.diarize(audio, sample_rate, channels)?; - - let segments = align_speakers(&transcription.tokens, &diarization_segments); - for segment in &segments { - println!( - "[{:.2}s - {:.2}s] {}: {}", - segment.start, segment.end, segment.speaker, segment.text - ); - } - Ok(segments) -} + let chunk_samples = samples_per_chunk(normalized.sample_rate, STREAM_CHUNK_MS); + let mut cumulative_audio: Vec<f32> = Vec::new(); + let mut last_printed_tokens = 0usize; + let mut final_segments: Vec<DialogueSegment> = Vec::new(); -fn load_audio(path: &Path) -> Result<(Vec<f32>, u32, u16), Box<dyn std::error::Error>> { - let mut reader = WavReader::open(path)?; - let spec = reader.spec(); + for (chunk_idx, chunk) in normalized.samples.chunks(chunk_samples).enumerate() { + cumulative_audio.extend_from_slice(chunk); - if spec.sample_rate != SAMPLE_RATE { - return Err(format!( - "Expected {} Hz audio, got {} Hz", - SAMPLE_RATE, spec.sample_rate - ) - .into()); - } + let diarization_segments = sortformer.diarize( + cumulative_audio.clone(), + normalized.sample_rate, + normalized.channels, + )?; + + let transcription = parakeet.transcribe_samples( + cumulative_audio.clone(), + normalized.sample_rate, + normalized.channels, + Some(TimestampMode::Sentences), + )?; + + final_segments = align_speakers(&transcription.tokens, &diarization_segments); - let samples = match spec.sample_format { - hound::SampleFormat::Float => reader.samples::<f32>().collect::<Result<Vec<_>, _>>()?, - hound::SampleFormat::Int => reader - .samples::<i16>() - .map(|s| s.map(|s| s as f32 / 32768.0)) - .collect::<Result<Vec<_>, _>>()?, - }; + // Simulate "live" output by printing only newly emitted tokens. + if transcription.tokens.len() > last_printed_tokens { + let new_segments = &final_segments[last_printed_tokens..]; + for segment in new_segments { + println!( + "[chunk {}] [{:.2}s - {:.2}s] {}: {}", + chunk_idx, segment.start, segment.end, segment.speaker, segment.text + ); + } + last_printed_tokens = transcription.tokens.len(); + } + } - Ok((samples, spec.sample_rate, spec.channels)) + Ok(final_segments) } fn align_speakers(tokens: &[TimedToken], speakers: &[SpeakerSegment]) -> Vec<DialogueSegment> { @@ -84,6 +82,25 @@ fn align_speakers(tokens: &[TimedToken], speakers: &[SpeakerSegment]) -> Vec<Dia .collect() } +fn samples_per_chunk(sample_rate: u32, chunk_ms: u32) -> usize { + let samples = (sample_rate as u64) + .saturating_mul(chunk_ms as u64) + .saturating_div(1_000); + samples.max(1) as usize +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn samples_per_chunk_rounds_down_and_clamps() { + assert_eq!(samples_per_chunk(16_000, 1_000), 16_000); + assert_eq!(samples_per_chunk(16_000, 160), 2_560); + assert_eq!(samples_per_chunk(16_000, 0), 1); + } +} + fn speaker_for_span(start: f32, end: f32, speakers: &[SpeakerSegment]) -> Option<String> { speakers .iter() diff --git a/makima/src/main.rs b/makima/src/main.rs index 9097ef6..2348b23 100644 --- a/makima/src/main.rs +++ b/makima/src/main.rs @@ -1,6 +1,44 @@ +use std::path::Path; +use crate::tts::{save_wav, ChatterboxTTS}; + +mod audio; mod listen; +pub mod tts; fn main() -> Result<(), Box<dyn std::error::Error>> { + println!("Loading ChatterboxTTS..."); + let mut tts = ChatterboxTTS::from_pretrained(None)?; + println!("Model loaded successfully!"); + + // // Voice cloning using existing audio file + // println!("Generating TTS with voice cloning..."); + // let audio = tts.generate_tts_with_voice( + // "Hello, this is a test of the voice cloning system.", + // Path::new("audio.wav") + // )?; + // + // println!("Generated {} samples", audio.len()); + // save_wav(&audio, Path::new("output.wav"))?; + // println!("Saved to output.wav"); + + + // Load reference audio from mp3 + println!("Loading reference audio..."); + let reference = audio::to_16k_mono_from_path(Path::new("audio.mp3"))?; + let samples = &reference.samples; + let sample_rate = reference.sample_rate; + + // Voice cloning using audio samples + println!("Generating TTS with voice cloning..."); + let audio = tts.generate_tts_with_samples( + "Hello, this is a test of the voice cloning system [chuckles]. Repeat after me \" I am Steve Jobs!\"", + samples, + sample_rate, + )?; + + println!("Generated {} samples", audio.len()); + save_wav(&audio, Path::new("output.wav"))?; + println!("Saved to output.wav"); let segments = listen::listen()?; println!("Captured {} diarized segments", segments.len()); Ok(()) 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(¤t_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); + } +} |