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"""
One-shot music generation functions for MagentaRT.
This module contains the core generation functions extracted from the main app
that can be used independently for single-shot music generation tasks.
"""
import math
import numpy as np
from magenta_rt import audio as au
from utils import (
match_loudness_to_reference,
stitch_generated,
hard_trim_seconds,
apply_micro_fades,
make_bar_aligned_context,
take_bar_aligned_tail
)
def generate_loop_continuation_with_mrt(
mrt,
input_wav_path: str,
bpm: float,
extra_styles=None,
style_weights=None,
bars: int = 8,
beats_per_bar: int = 4,
loop_weight: float = 1.0,
loudness_mode: str = "auto",
loudness_headroom_db: float = 1.0,
intro_bars_to_drop: int = 0,
):
"""
Generate a continuation of an input loop using MagentaRT.
Args:
mrt: MagentaRT instance
input_wav_path: Path to input audio file
bpm: Beats per minute
extra_styles: List of additional text style prompts (optional)
style_weights: List of weights for style prompts (optional)
bars: Number of bars to generate
beats_per_bar: Beats per bar (typically 4)
loop_weight: Weight for the input loop's style embedding
loudness_mode: Loudness matching method ("auto", "lufs", "rms", "none")
loudness_headroom_db: Headroom in dB for peak limiting
intro_bars_to_drop: Number of intro bars to generate then drop
Returns:
Tuple of (au.Waveform output, dict loudness_stats)
"""
# Load & prep (unchanged)
loop = au.Waveform.from_file(input_wav_path).resample(mrt.sample_rate).as_stereo()
# Use tail for context (your recent change)
codec_fps = float(mrt.codec.frame_rate)
ctx_seconds = float(mrt.config.context_length_frames) / codec_fps
loop_for_context = take_bar_aligned_tail(loop, bpm, beats_per_bar, ctx_seconds)
tokens_full = mrt.codec.encode(loop_for_context).astype(np.int32)
tokens = tokens_full[:, :mrt.config.decoder_codec_rvq_depth]
# Bar-aligned token window (unchanged)
context_tokens = make_bar_aligned_context(
tokens, bpm=bpm, fps=float(mrt.codec.frame_rate),
ctx_frames=mrt.config.context_length_frames, beats_per_bar=beats_per_bar
)
state = mrt.init_state()
state.context_tokens = context_tokens
# STYLE embed (optional: switch to loop_for_context if you want stronger "recent" bias)
loop_embed = mrt.embed_style(loop_for_context)
embeds, weights = [loop_embed], [float(loop_weight)]
if extra_styles:
for i, s in enumerate(extra_styles):
if s.strip():
embeds.append(mrt.embed_style(s.strip()))
w = style_weights[i] if (style_weights and i < len(style_weights)) else 1.0
weights.append(float(w))
wsum = float(sum(weights)) or 1.0
weights = [w / wsum for w in weights]
combined_style = np.sum([w * e for w, e in zip(weights, embeds)], axis=0).astype(loop_embed.dtype)
# --- Length math ---
seconds_per_bar = beats_per_bar * (60.0 / bpm)
total_secs = bars * seconds_per_bar
drop_bars = max(0, int(intro_bars_to_drop))
drop_secs = min(drop_bars, bars) * seconds_per_bar # clamp to <= bars
gen_total_secs = total_secs + drop_secs # generate extra
# Chunk scheduling to cover gen_total_secs
chunk_secs = mrt.config.chunk_length_frames * mrt.config.frame_length_samples / mrt.sample_rate # ~2.0
steps = int(math.ceil(gen_total_secs / chunk_secs)) + 1 # pad then trim
# Generate
chunks = []
for _ in range(steps):
wav, state = mrt.generate_chunk(state=state, style=combined_style)
chunks.append(wav)
# Stitch continuous audio
stitched = stitch_generated(chunks, mrt.sample_rate, mrt.config.crossfade_length).as_stereo()
# Trim to generated length (bars + dropped bars)
stitched = hard_trim_seconds(stitched, gen_total_secs)
# 👉 Drop the intro bars
if drop_secs > 0:
n_drop = int(round(drop_secs * stitched.sample_rate))
stitched = au.Waveform(stitched.samples[n_drop:], stitched.sample_rate)
# Final exact-length trim to requested bars
out = hard_trim_seconds(stitched, total_secs)
# Final polish AFTER drop
out = out.peak_normalize(0.95)
apply_micro_fades(out, 5)
# Loudness match to input (after drop) so bar 1 sits right
out, loud_stats = match_loudness_to_reference(
ref=loop, target=out,
method=loudness_mode, headroom_db=loudness_headroom_db
)
return out, loud_stats
def generate_style_only_with_mrt(
mrt,
bpm: float,
bars: int = 8,
beats_per_bar: int = 4,
styles: str = "warmup",
style_weights: str = "",
intro_bars_to_drop: int = 0,
):
"""
Style-only, bar-aligned generation using a silent context (no input audio).
Returns: (au.Waveform out, dict loud_stats_or_None)
"""
# ---- Build a 10s silent context, tokenized for the model ----
codec_fps = float(mrt.codec.frame_rate)
ctx_seconds = float(mrt.config.context_length_frames) / codec_fps
sr = int(mrt.sample_rate)
silent = au.Waveform(np.zeros((int(round(ctx_seconds * sr)), 2), np.float32), sr)
tokens_full = mrt.codec.encode(silent).astype(np.int32)
tokens = tokens_full[:, :mrt.config.decoder_codec_rvq_depth]
state = mrt.init_state()
state.context_tokens = tokens
# ---- Style vector (text prompts only, normalized weights) ----
prompts = [s.strip() for s in (styles.split(",") if styles else []) if s.strip()]
if not prompts:
prompts = ["warmup"]
sw = [float(x) for x in style_weights.split(",")] if style_weights else []
embeds, weights = [], []
for i, p in enumerate(prompts):
embeds.append(mrt.embed_style(p))
weights.append(sw[i] if i < len(sw) else 1.0)
wsum = float(sum(weights)) or 1.0
weights = [w / wsum for w in weights]
style_vec = np.sum([w * e for w, e in zip(weights, embeds)], axis=0).astype(np.float32)
# ---- Target length math ----
seconds_per_bar = beats_per_bar * (60.0 / bpm)
total_secs = bars * seconds_per_bar
drop_bars = max(0, int(intro_bars_to_drop))
drop_secs = min(drop_bars, bars) * seconds_per_bar
gen_total_secs = total_secs + drop_secs
# ~2.0s chunk length from model config
chunk_secs = (mrt.config.chunk_length_frames * mrt.config.frame_length_samples) / float(mrt.sample_rate)
# Generate enough chunks to cover total, plus a pad chunk for crossfade headroom
steps = int(math.ceil(gen_total_secs / chunk_secs)) + 1
chunks = []
for _ in range(steps):
wav, state = mrt.generate_chunk(state=state, style=style_vec)
chunks.append(wav)
# Stitch & trim to exact musical length
stitched = stitch_generated(chunks, mrt.sample_rate, mrt.config.crossfade_length).as_stereo()
stitched = hard_trim_seconds(stitched, gen_total_secs)
if drop_secs > 0:
n_drop = int(round(drop_secs * stitched.sample_rate))
stitched = au.Waveform(stitched.samples[n_drop:], stitched.sample_rate)
out = hard_trim_seconds(stitched, total_secs)
out = out.peak_normalize(0.95)
apply_micro_fades(out, 5)
return out, None # loudness stats not applicable (no reference) |