feat: add voice cloning pipeline (S3 audio → Whisper → VITS training → Gitea)
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686
voice_cloning_pipeline.py
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686
voice_cloning_pipeline.py
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#!/usr/bin/env python3
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"""
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Voice Cloning Pipeline – Kubeflow Pipelines SDK
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Takes an audio file and a transcript, extracts a target speaker's
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segments, preprocesses into LJSpeech-format training data, fine-tunes
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a Coqui VITS voice model, pushes the model to Gitea, and logs to MLflow.
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Usage:
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pip install kfp==2.12.1
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python voice_cloning_pipeline.py
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# Upload voice_cloning_pipeline.yaml to Kubeflow Pipelines UI
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"""
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from kfp import compiler, dsl
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from typing import NamedTuple
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# ──────────────────────────────────────────────────────────────
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# 1. Transcribe + diarise audio via Whisper to identify speakers
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# ──────────────────────────────────────────────────────────────
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@dsl.component(
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base_image="python:3.13-slim",
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packages_to_install=["requests", "boto3"],
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)
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def transcribe_and_diarise(
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s3_endpoint: str,
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s3_bucket: str,
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s3_key: str,
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whisper_url: str = "http://ai-inference-serve-svc.kuberay.svc.cluster.local:8000/whisper",
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) -> NamedTuple("TranscriptOutput", [("transcript_json", str), ("speakers", str), ("audio_path", str)]):
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"""Download audio from Quobjects S3, transcribe via Whisper with timestamps."""
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import json
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import os
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import subprocess
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import tempfile
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import base64
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import boto3
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import requests
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out = NamedTuple("TranscriptOutput", [("transcript_json", str), ("speakers", str), ("audio_path", str)])
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work = tempfile.mkdtemp()
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# ── Download audio from Quobjects S3 ─────────────────────
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ext = os.path.splitext(s3_key)[-1] or ".wav"
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audio_path = os.path.join(work, f"audio_raw{ext}")
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client = boto3.client(
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"s3",
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endpoint_url=f"http://{s3_endpoint}",
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aws_access_key_id="",
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aws_secret_access_key="",
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config=boto3.session.Config(signature_version="UNSIGNED"),
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)
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print(f"Downloading s3://{s3_bucket}/{s3_key} from {s3_endpoint}")
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client.download_file(s3_bucket, s3_key, audio_path)
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print(f"Downloaded {os.path.getsize(audio_path)} bytes")
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# ── Normalise to 16 kHz mono WAV ─────────────────────────
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wav_path = os.path.join(work, "audio.wav")
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subprocess.run(
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["apt-get", "update", "-qq"],
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capture_output=True,
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)
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subprocess.run(
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["apt-get", "install", "-y", "-qq", "ffmpeg"],
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capture_output=True, check=True,
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)
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subprocess.run(
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["ffmpeg", "-y", "-i", audio_path, "-ac", "1",
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"-ar", "16000", "-sample_fmt", "s16", wav_path],
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capture_output=True, check=True,
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)
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# ── Send to Whisper for timestamped transcription ─────────
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with open(wav_path, "rb") as f:
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audio_b64 = base64.b64encode(f.read()).decode()
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payload = {
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"audio": audio_b64,
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"response_format": "verbose_json",
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"timestamp_granularities": ["segment"],
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}
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resp = requests.post(whisper_url, json=payload, timeout=600)
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resp.raise_for_status()
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result = resp.json()
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segments = result.get("segments", [])
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print(f"Whisper returned {len(segments)} segments")
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# ── Group segments by speaker if diarisation is present ───
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# Whisper may not diarise, but we still produce segments with
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# start/end timestamps that the next step can use.
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speakers = set()
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for i, seg in enumerate(segments):
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spk = seg.get("speaker", f"SPEAKER_{i // 10}")
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seg["speaker"] = spk
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speakers.add(spk)
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speakers_list = sorted(speakers)
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print(f"Detected speakers: {speakers_list}")
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return out(
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transcript_json=json.dumps(segments),
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speakers=json.dumps(speakers_list),
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audio_path=wav_path,
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)
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# ──────────────────────────────────────────────────────────────
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# 2. Extract target speaker's audio segments
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# ──────────────────────────────────────────────────────────────
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@dsl.component(
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base_image="python:3.13-slim",
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packages_to_install=[],
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)
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def extract_speaker_segments(
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transcript_json: str,
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audio_path: str,
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target_speaker: str,
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min_duration_s: float = 1.0,
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max_duration_s: float = 15.0,
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) -> NamedTuple("SpeakerSegments", [("segments_json", str), ("num_segments", int), ("total_duration_s", float)]):
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"""Slice the audio into per-utterance WAV files for the target speaker."""
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import json
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import os
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import subprocess
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import tempfile
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out = NamedTuple("SpeakerSegments", [("segments_json", str), ("num_segments", int), ("total_duration_s", float)])
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work = tempfile.mkdtemp()
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wavs_dir = os.path.join(work, "wavs")
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os.makedirs(wavs_dir, exist_ok=True)
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# Install ffmpeg
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subprocess.run(["apt-get", "update", "-qq"], capture_output=True)
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subprocess.run(["apt-get", "install", "-y", "-qq", "ffmpeg"], capture_output=True, check=True)
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segments = json.loads(transcript_json)
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# Filter by speaker — fuzzy match (case-insensitive, partial)
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target_lower = target_speaker.lower()
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matched = []
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for seg in segments:
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spk = seg.get("speaker", "").lower()
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if target_lower in spk or spk in target_lower:
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matched.append(seg)
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# If no speaker labels matched, the user may have given a name
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# that doesn't appear. Fall back to using ALL segments.
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if not matched:
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print(f"WARNING: No segments matched speaker '{target_speaker}'. "
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f"Using all {len(segments)} segments.")
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matched = segments
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print(f"Matched {len(matched)} segments for speaker '{target_speaker}'")
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kept = []
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total_dur = 0.0
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for i, seg in enumerate(matched):
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start = float(seg.get("start", 0))
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end = float(seg.get("end", start + 5))
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duration = end - start
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text = seg.get("text", "").strip()
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if duration < min_duration_s or not text:
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continue
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if duration > max_duration_s:
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end = start + max_duration_s
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duration = max_duration_s
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wav_name = f"utt_{i:04d}.wav"
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wav_out = os.path.join(wavs_dir, wav_name)
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subprocess.run(
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["ffmpeg", "-y", "-i", audio_path,
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"-ss", str(start), "-to", str(end),
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"-ac", "1", "-ar", "22050", "-sample_fmt", "s16",
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wav_out],
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capture_output=True, check=True,
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)
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kept.append({
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"wav": wav_name,
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"text": text,
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"start": start,
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"end": end,
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"duration": round(duration, 2),
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})
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total_dur += duration
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print(f"Extracted {len(kept)} utterances, total {total_dur:.1f}s")
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return out(
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segments_json=json.dumps({"wavs_dir": wavs_dir, "utterances": kept}),
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num_segments=len(kept),
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total_duration_s=round(total_dur, 2),
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)
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# ──────────────────────────────────────────────────────────────
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# 3. Prepare LJSpeech-format dataset for Coqui TTS
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# ──────────────────────────────────────────────────────────────
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@dsl.component(
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base_image="python:3.13-slim",
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packages_to_install=[],
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)
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def prepare_ljspeech_dataset(
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segments_json: str,
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voice_name: str,
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language: str = "en",
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) -> NamedTuple("DatasetOutput", [("dataset_dir", str), ("num_samples", int)]):
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"""Create metadata.csv + wavs/ in LJSpeech format."""
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import json
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import os
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import shutil
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out = NamedTuple("DatasetOutput", [("dataset_dir", str), ("num_samples", int)])
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data = json.loads(segments_json)
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wavs_src = data["wavs_dir"]
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utterances = data["utterances"]
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dataset_dir = os.path.join(os.path.dirname(wavs_src), "dataset")
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wavs_dst = os.path.join(dataset_dir, "wavs")
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os.makedirs(wavs_dst, exist_ok=True)
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lines = []
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for utt in utterances:
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src = os.path.join(wavs_src, utt["wav"])
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dst = os.path.join(wavs_dst, utt["wav"])
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shutil.copy2(src, dst)
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stem = os.path.splitext(utt["wav"])[0]
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# LJSpeech format: id|text|text
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text = utt["text"].replace("|", " ")
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lines.append(f"{stem}|{text}|{text}")
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metadata_path = os.path.join(dataset_dir, "metadata.csv")
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with open(metadata_path, "w", encoding="utf-8") as f:
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f.write("\n".join(lines))
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# Dataset config for reference
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import json as _json
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config = {
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"name": voice_name,
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"language": language,
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"num_samples": len(lines),
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"format": "ljspeech",
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"sample_rate": 22050,
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}
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with open(os.path.join(dataset_dir, "dataset_config.json"), "w") as f:
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_json.dump(config, f, indent=2)
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print(f"LJSpeech dataset ready: {len(lines)} samples")
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return out(dataset_dir=dataset_dir, num_samples=len(lines))
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# ──────────────────────────────────────────────────────────────
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# 4. Fine-tune Coqui VITS voice model
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# ──────────────────────────────────────────────────────────────
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@dsl.component(
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base_image="ghcr.io/coqui-ai/tts:latest",
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packages_to_install=[],
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)
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def train_vits_voice(
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dataset_dir: str,
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voice_name: str,
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language: str = "en",
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base_model: str = "tts_models/en/ljspeech/vits",
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num_epochs: int = 100,
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batch_size: int = 16,
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learning_rate: float = 0.0001,
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) -> NamedTuple("TrainOutput", [("model_dir", str), ("best_checkpoint", str), ("final_loss", float)]):
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"""Fine-tune a VITS model on the speaker dataset."""
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import os
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import json
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import glob
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out = NamedTuple("TrainOutput", [("model_dir", str), ("best_checkpoint", str), ("final_loss", float)])
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OUTPUT_DIR = "/tmp/vits_output"
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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print(f"=== Coqui VITS Voice Training ===")
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print(f"Voice name : {voice_name}")
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print(f"Base model : {base_model}")
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print(f"Dataset : {dataset_dir}")
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print(f"Epochs : {num_epochs}")
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print(f"Batch size : {batch_size}")
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print(f"LR : {learning_rate}")
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# ── Download base model checkpoint ────────────────────────
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restore_path = None
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if base_model and base_model != "none":
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from TTS.utils.manage import ModelManager
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manager = ModelManager()
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model_path, config_path, _ = manager.download_model(base_model)
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restore_path = model_path
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print(f"Base model checkpoint: {restore_path}")
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# ── Configure and train ───────────────────────────────────
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from trainer import Trainer, TrainerArgs
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from TTS.tts.configs.vits_config import VitsConfig
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from TTS.tts.configs.shared_configs import BaseDatasetConfig
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from TTS.tts.datasets import load_tts_samples
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from TTS.tts.models.vits import Vits
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from TTS.tts.utils.text.tokenizer import TTSTokenizer
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from TTS.utils.audio import AudioProcessor
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dataset_config = BaseDatasetConfig(
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formatter="ljspeech",
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meta_file_train="metadata.csv",
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path=dataset_dir,
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language=language,
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)
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config = VitsConfig(
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run_name=voice_name,
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output_path=OUTPUT_DIR,
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datasets=[dataset_config],
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batch_size=batch_size,
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eval_batch_size=max(1, batch_size // 2),
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num_loader_workers=4,
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num_eval_loader_workers=2,
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run_eval=True,
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test_delay_epochs=5,
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epochs=num_epochs,
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text_cleaner="phoneme_cleaners",
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use_phonemes=True,
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phoneme_language=language,
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phoneme_cache_path=os.path.join(OUTPUT_DIR, "phoneme_cache"),
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compute_input_seq_cache=True,
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print_step=25,
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print_eval=False,
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mixed_precision=True,
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save_step=500,
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save_n_checkpoints=3,
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save_best_after=1000,
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lr=learning_rate,
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audio={
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"sample_rate": 22050,
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"resample": True,
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"do_trim_silence": True,
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"trim_db": 45,
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},
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)
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ap = AudioProcessor.init_from_config(config)
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tokenizer, config = TTSTokenizer.init_from_config(config)
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train_samples, eval_samples = load_tts_samples(
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dataset_config,
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eval_split=True,
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eval_split_max_size=config.eval_split_max_size,
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eval_split_size=config.eval_split_size,
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)
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print(f"Training samples: {len(train_samples)}")
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print(f"Eval samples: {len(eval_samples)}")
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model = Vits(config, ap, tokenizer, speaker_manager=None)
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trainer_args = TrainerArgs(
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restore_path=restore_path,
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skip_train_epoch=False,
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)
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trainer = Trainer(
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trainer_args,
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config,
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output_path=OUTPUT_DIR,
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model=model,
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train_samples=train_samples,
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eval_samples=eval_samples,
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)
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trainer.fit()
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# ── Find best checkpoint ──────────────────────────────────
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best_files = glob.glob(os.path.join(OUTPUT_DIR, "**/best_model*.pth"), recursive=True)
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if not best_files:
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best_files = glob.glob(os.path.join(OUTPUT_DIR, "**/*.pth"), recursive=True)
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best_files.sort(key=os.path.getmtime, reverse=True)
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best_checkpoint = best_files[0] if best_files else ""
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# Try to read final loss from trainer
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final_loss = 0.0
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try:
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final_loss = float(trainer.keep_avg_train["avg_loss"])
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except Exception:
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pass
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print(f"Training complete. Best checkpoint: {best_checkpoint}")
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print(f"Final loss: {final_loss:.4f}")
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return out(model_dir=OUTPUT_DIR, best_checkpoint=best_checkpoint, final_loss=final_loss)
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# ──────────────────────────────────────────────────────────────
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# 5. Push trained voice model to Gitea repository
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# ──────────────────────────────────────────────────────────────
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@dsl.component(
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base_image="python:3.13-slim",
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packages_to_install=["requests"],
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)
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def push_model_to_gitea(
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model_dir: str,
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voice_name: str,
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gitea_url: str = "http://gitea-http.gitea.svc.cluster.local:3000",
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gitea_owner: str = "daviestechlabs",
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gitea_repo: str = "voice-models",
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gitea_username: str = "",
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gitea_password: str = "",
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) -> NamedTuple("PushOutput", [("repo_url", str), ("files_pushed", int)]):
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"""Package and push the trained model to a Gitea repository."""
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import base64
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import glob
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import json
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import os
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import requests
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out = NamedTuple("PushOutput", [("repo_url", str), ("files_pushed", int)])
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session = requests.Session()
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session.auth = (gitea_username, gitea_password) if gitea_username else None
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api = f"{gitea_url}/api/v1"
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repo_url = f"{gitea_url}/{gitea_owner}/{gitea_repo}"
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# ── Ensure repo exists ────────────────────────────────────
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r = session.get(f"{api}/repos/{gitea_owner}/{gitea_repo}", timeout=30)
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if r.status_code == 404:
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print(f"Creating repository: {gitea_owner}/{gitea_repo}")
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r = session.post(
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f"{api}/orgs/{gitea_owner}/repos",
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json={
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"name": gitea_repo,
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"description": "Trained voice models from voice cloning pipeline",
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"private": False,
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"auto_init": True,
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},
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timeout=30,
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)
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if r.status_code not in (200, 201):
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r = session.post(
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f"{api}/user/repos",
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json={"name": gitea_repo, "description": "Trained voice models", "auto_init": True},
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timeout=30,
|
||||
)
|
||||
r.raise_for_status()
|
||||
print("Repository created")
|
||||
|
||||
# ── Collect model files ───────────────────────────────────
|
||||
files_to_push = []
|
||||
|
||||
# Best model checkpoint
|
||||
for pattern in ["**/best_model*.pth", "**/*.pth"]:
|
||||
found = glob.glob(os.path.join(model_dir, pattern), recursive=True)
|
||||
if found:
|
||||
found.sort(key=os.path.getmtime, reverse=True)
|
||||
files_to_push.append(found[0])
|
||||
break
|
||||
|
||||
# Config
|
||||
for pattern in ["**/config.json"]:
|
||||
found = glob.glob(os.path.join(model_dir, pattern), recursive=True)
|
||||
if found:
|
||||
files_to_push.append(found[0])
|
||||
|
||||
# Model info
|
||||
model_info = {
|
||||
"name": voice_name,
|
||||
"type": "coqui-vits",
|
||||
"base_model": "tts_models/en/ljspeech/vits",
|
||||
"sample_rate": 22050,
|
||||
}
|
||||
info_path = os.path.join(model_dir, "model_info.json")
|
||||
with open(info_path, "w") as f:
|
||||
json.dump(model_info, f, indent=2)
|
||||
files_to_push.append(info_path)
|
||||
|
||||
# ── Push each file ────────────────────────────────────────
|
||||
pushed = 0
|
||||
for fpath in files_to_push:
|
||||
rel = os.path.relpath(fpath, model_dir)
|
||||
gitea_path = f"{voice_name}/{rel}"
|
||||
print(f"Pushing: {gitea_path} ({os.path.getsize(fpath)} bytes)")
|
||||
|
||||
with open(fpath, "rb") as f:
|
||||
content_b64 = base64.b64encode(f.read()).decode()
|
||||
|
||||
# Check if file exists
|
||||
r = session.get(
|
||||
f"{api}/repos/{gitea_owner}/{gitea_repo}/contents/{gitea_path}",
|
||||
timeout=30,
|
||||
)
|
||||
|
||||
payload = {
|
||||
"content": content_b64,
|
||||
"message": f"Upload {voice_name}: {rel}",
|
||||
}
|
||||
|
||||
if r.status_code == 200:
|
||||
sha = r.json().get("sha", "")
|
||||
payload["sha"] = sha
|
||||
r = session.put(
|
||||
f"{api}/repos/{gitea_owner}/{gitea_repo}/contents/{gitea_path}",
|
||||
json=payload, timeout=120,
|
||||
)
|
||||
else:
|
||||
r = session.post(
|
||||
f"{api}/repos/{gitea_owner}/{gitea_repo}/contents/{gitea_path}",
|
||||
json=payload, timeout=120,
|
||||
)
|
||||
|
||||
if r.status_code in (200, 201):
|
||||
pushed += 1
|
||||
print(f" ✓ Pushed")
|
||||
else:
|
||||
print(f" ✗ Failed ({r.status_code}): {r.text[:200]}")
|
||||
|
||||
print(f"\nPushed {pushed}/{len(files_to_push)} files to {repo_url}")
|
||||
return out(repo_url=repo_url, files_pushed=pushed)
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# 6. Log metrics to MLflow
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
@dsl.component(
|
||||
base_image="python:3.13-slim",
|
||||
packages_to_install=["mlflow>=2.10.0", "requests"],
|
||||
)
|
||||
def log_training_metrics(
|
||||
voice_name: str,
|
||||
num_segments: int,
|
||||
total_duration_s: float,
|
||||
final_loss: float,
|
||||
num_epochs: int,
|
||||
batch_size: int,
|
||||
learning_rate: float,
|
||||
repo_url: str,
|
||||
files_pushed: int,
|
||||
mlflow_tracking_uri: str = "http://mlflow.mlflow.svc.cluster.local:80",
|
||||
experiment_name: str = "voice-cloning",
|
||||
) -> NamedTuple("LogOutput", [("run_id", str)]):
|
||||
"""Log training run to MLflow."""
|
||||
import mlflow
|
||||
from datetime import datetime
|
||||
|
||||
out = NamedTuple("LogOutput", [("run_id", str)])
|
||||
|
||||
mlflow.set_tracking_uri(mlflow_tracking_uri)
|
||||
mlflow.set_experiment(experiment_name)
|
||||
|
||||
with mlflow.start_run(run_name=f"voice-clone-{voice_name}-{datetime.now():%Y%m%d-%H%M}") as run:
|
||||
mlflow.log_params({
|
||||
"voice_name": voice_name,
|
||||
"base_model": "tts_models/en/ljspeech/vits",
|
||||
"model_type": "coqui-vits",
|
||||
"num_epochs": num_epochs,
|
||||
"batch_size": batch_size,
|
||||
"learning_rate": learning_rate,
|
||||
"sample_rate": 22050,
|
||||
})
|
||||
mlflow.log_metrics({
|
||||
"num_training_segments": num_segments,
|
||||
"total_audio_duration_s": total_duration_s,
|
||||
"final_loss": final_loss,
|
||||
"files_pushed": files_pushed,
|
||||
})
|
||||
mlflow.set_tags({
|
||||
"pipeline": "voice-cloning",
|
||||
"gitea_repo": repo_url,
|
||||
"voice_name": voice_name,
|
||||
})
|
||||
print(f"Logged to MLflow run: {run.info.run_id}")
|
||||
return out(run_id=run.info.run_id)
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# Pipeline definition
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
@dsl.pipeline(
|
||||
name="Voice Cloning Pipeline",
|
||||
description=(
|
||||
"Extract a speaker from audio+transcript, fine-tune a Coqui VITS "
|
||||
"voice model, push to Gitea, and log metrics to MLflow."
|
||||
),
|
||||
)
|
||||
def voice_cloning_pipeline(
|
||||
s3_endpoint: str = "candlekeep.lab.daviestechlabs.io",
|
||||
s3_bucket: str = "training-data",
|
||||
s3_key: str = "",
|
||||
target_speaker: str = "SPEAKER_0",
|
||||
voice_name: str = "custom-voice",
|
||||
language: str = "en",
|
||||
base_model: str = "tts_models/en/ljspeech/vits",
|
||||
num_epochs: int = 100,
|
||||
batch_size: int = 16,
|
||||
learning_rate: float = 0.0001,
|
||||
min_segment_duration_s: float = 1.0,
|
||||
max_segment_duration_s: float = 15.0,
|
||||
# Whisper / inference endpoints
|
||||
whisper_url: str = "http://ai-inference-serve-svc.kuberay.svc.cluster.local:8000/whisper",
|
||||
# Gitea
|
||||
gitea_url: str = "http://gitea-http.gitea.svc.cluster.local:3000",
|
||||
gitea_owner: str = "daviestechlabs",
|
||||
gitea_repo: str = "voice-models",
|
||||
gitea_username: str = "",
|
||||
gitea_password: str = "",
|
||||
# MLflow
|
||||
mlflow_tracking_uri: str = "http://mlflow.mlflow.svc.cluster.local:80",
|
||||
):
|
||||
# 1 - Download from Quobjects S3 and transcribe with Whisper
|
||||
transcribed = transcribe_and_diarise(
|
||||
s3_endpoint=s3_endpoint,
|
||||
s3_bucket=s3_bucket,
|
||||
s3_key=s3_key,
|
||||
whisper_url=whisper_url,
|
||||
)
|
||||
|
||||
# 2 - Extract target speaker's segments
|
||||
extracted = extract_speaker_segments(
|
||||
transcript_json=transcribed.outputs["transcript_json"],
|
||||
audio_path=transcribed.outputs["audio_path"],
|
||||
target_speaker=target_speaker,
|
||||
min_duration_s=min_segment_duration_s,
|
||||
max_duration_s=max_segment_duration_s,
|
||||
)
|
||||
|
||||
# 3 - Build LJSpeech dataset
|
||||
dataset = prepare_ljspeech_dataset(
|
||||
segments_json=extracted.outputs["segments_json"],
|
||||
voice_name=voice_name,
|
||||
language=language,
|
||||
)
|
||||
|
||||
# 4 - Train VITS model
|
||||
trained = train_vits_voice(
|
||||
dataset_dir=dataset.outputs["dataset_dir"],
|
||||
voice_name=voice_name,
|
||||
language=language,
|
||||
base_model=base_model,
|
||||
num_epochs=num_epochs,
|
||||
batch_size=batch_size,
|
||||
learning_rate=learning_rate,
|
||||
)
|
||||
trained.set_accelerator_type("gpu")
|
||||
trained.set_gpu_limit(1)
|
||||
trained.set_memory_request("16Gi")
|
||||
trained.set_memory_limit("32Gi")
|
||||
trained.set_cpu_request("4")
|
||||
trained.set_cpu_limit("8")
|
||||
|
||||
# 5 - Push model to Gitea
|
||||
pushed = push_model_to_gitea(
|
||||
model_dir=trained.outputs["model_dir"],
|
||||
voice_name=voice_name,
|
||||
gitea_url=gitea_url,
|
||||
gitea_owner=gitea_owner,
|
||||
gitea_repo=gitea_repo,
|
||||
gitea_username=gitea_username,
|
||||
gitea_password=gitea_password,
|
||||
)
|
||||
|
||||
# 6 - Log to MLflow
|
||||
log_training_metrics(
|
||||
voice_name=voice_name,
|
||||
num_segments=extracted.outputs["num_segments"],
|
||||
total_duration_s=extracted.outputs["total_duration_s"],
|
||||
final_loss=trained.outputs["final_loss"],
|
||||
num_epochs=num_epochs,
|
||||
batch_size=batch_size,
|
||||
learning_rate=learning_rate,
|
||||
repo_url=pushed.outputs["repo_url"],
|
||||
files_pushed=pushed.outputs["files_pushed"],
|
||||
mlflow_tracking_uri=mlflow_tracking_uri,
|
||||
)
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# Compile
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
if __name__ == "__main__":
|
||||
compiler.Compiler().compile(
|
||||
pipeline_func=voice_cloning_pipeline,
|
||||
package_path="voice_cloning_pipeline.yaml",
|
||||
)
|
||||
print("Compiled: voice_cloning_pipeline.yaml")
|
||||
876
voice_cloning_pipeline.yaml
Normal file
876
voice_cloning_pipeline.yaml
Normal file
@@ -0,0 +1,876 @@
|
||||
# PIPELINE DEFINITION
|
||||
# Name: voice-cloning-pipeline
|
||||
# Description: Extract a speaker from audio+transcript, fine-tune a Coqui VITS voice model, push to Gitea, and log metrics to MLflow.
|
||||
# Inputs:
|
||||
# base_model: str [Default: 'tts_models/en/ljspeech/vits']
|
||||
# batch_size: int [Default: 16.0]
|
||||
# gitea_owner: str [Default: 'daviestechlabs']
|
||||
# gitea_password: str [Default: '']
|
||||
# gitea_repo: str [Default: 'voice-models']
|
||||
# gitea_url: str [Default: 'http://gitea-http.gitea.svc.cluster.local:3000']
|
||||
# gitea_username: str [Default: '']
|
||||
# language: str [Default: 'en']
|
||||
# learning_rate: float [Default: 0.0001]
|
||||
# max_segment_duration_s: float [Default: 15.0]
|
||||
# min_segment_duration_s: float [Default: 1.0]
|
||||
# mlflow_tracking_uri: str [Default: 'http://mlflow.mlflow.svc.cluster.local:80']
|
||||
# num_epochs: int [Default: 100.0]
|
||||
# s3_bucket: str [Default: 'training-data']
|
||||
# s3_endpoint: str [Default: 'candlekeep.lab.daviestechlabs.io']
|
||||
# s3_key: str [Default: '']
|
||||
# target_speaker: str [Default: 'SPEAKER_0']
|
||||
# voice_name: str [Default: 'custom-voice']
|
||||
# whisper_url: str [Default: 'http://ai-inference-serve-svc.kuberay.svc.cluster.local:8000/whisper']
|
||||
components:
|
||||
comp-extract-speaker-segments:
|
||||
executorLabel: exec-extract-speaker-segments
|
||||
inputDefinitions:
|
||||
parameters:
|
||||
audio_path:
|
||||
parameterType: STRING
|
||||
max_duration_s:
|
||||
defaultValue: 15.0
|
||||
isOptional: true
|
||||
parameterType: NUMBER_DOUBLE
|
||||
min_duration_s:
|
||||
defaultValue: 1.0
|
||||
isOptional: true
|
||||
parameterType: NUMBER_DOUBLE
|
||||
target_speaker:
|
||||
parameterType: STRING
|
||||
transcript_json:
|
||||
parameterType: STRING
|
||||
outputDefinitions:
|
||||
parameters:
|
||||
num_segments:
|
||||
parameterType: NUMBER_INTEGER
|
||||
segments_json:
|
||||
parameterType: STRING
|
||||
total_duration_s:
|
||||
parameterType: NUMBER_DOUBLE
|
||||
comp-log-training-metrics:
|
||||
executorLabel: exec-log-training-metrics
|
||||
inputDefinitions:
|
||||
parameters:
|
||||
batch_size:
|
||||
parameterType: NUMBER_INTEGER
|
||||
experiment_name:
|
||||
defaultValue: voice-cloning
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
files_pushed:
|
||||
parameterType: NUMBER_INTEGER
|
||||
final_loss:
|
||||
parameterType: NUMBER_DOUBLE
|
||||
learning_rate:
|
||||
parameterType: NUMBER_DOUBLE
|
||||
mlflow_tracking_uri:
|
||||
defaultValue: http://mlflow.mlflow.svc.cluster.local:80
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
num_epochs:
|
||||
parameterType: NUMBER_INTEGER
|
||||
num_segments:
|
||||
parameterType: NUMBER_INTEGER
|
||||
repo_url:
|
||||
parameterType: STRING
|
||||
total_duration_s:
|
||||
parameterType: NUMBER_DOUBLE
|
||||
voice_name:
|
||||
parameterType: STRING
|
||||
outputDefinitions:
|
||||
parameters:
|
||||
run_id:
|
||||
parameterType: STRING
|
||||
comp-prepare-ljspeech-dataset:
|
||||
executorLabel: exec-prepare-ljspeech-dataset
|
||||
inputDefinitions:
|
||||
parameters:
|
||||
language:
|
||||
defaultValue: en
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
segments_json:
|
||||
parameterType: STRING
|
||||
voice_name:
|
||||
parameterType: STRING
|
||||
outputDefinitions:
|
||||
parameters:
|
||||
dataset_dir:
|
||||
parameterType: STRING
|
||||
num_samples:
|
||||
parameterType: NUMBER_INTEGER
|
||||
comp-push-model-to-gitea:
|
||||
executorLabel: exec-push-model-to-gitea
|
||||
inputDefinitions:
|
||||
parameters:
|
||||
gitea_owner:
|
||||
defaultValue: daviestechlabs
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
gitea_password:
|
||||
defaultValue: ''
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
gitea_repo:
|
||||
defaultValue: voice-models
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
gitea_url:
|
||||
defaultValue: http://gitea-http.gitea.svc.cluster.local:3000
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
gitea_username:
|
||||
defaultValue: ''
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
model_dir:
|
||||
parameterType: STRING
|
||||
voice_name:
|
||||
parameterType: STRING
|
||||
outputDefinitions:
|
||||
parameters:
|
||||
files_pushed:
|
||||
parameterType: NUMBER_INTEGER
|
||||
repo_url:
|
||||
parameterType: STRING
|
||||
comp-train-vits-voice:
|
||||
executorLabel: exec-train-vits-voice
|
||||
inputDefinitions:
|
||||
parameters:
|
||||
base_model:
|
||||
defaultValue: tts_models/en/ljspeech/vits
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
batch_size:
|
||||
defaultValue: 16.0
|
||||
isOptional: true
|
||||
parameterType: NUMBER_INTEGER
|
||||
dataset_dir:
|
||||
parameterType: STRING
|
||||
language:
|
||||
defaultValue: en
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
learning_rate:
|
||||
defaultValue: 0.0001
|
||||
isOptional: true
|
||||
parameterType: NUMBER_DOUBLE
|
||||
num_epochs:
|
||||
defaultValue: 100.0
|
||||
isOptional: true
|
||||
parameterType: NUMBER_INTEGER
|
||||
voice_name:
|
||||
parameterType: STRING
|
||||
outputDefinitions:
|
||||
parameters:
|
||||
best_checkpoint:
|
||||
parameterType: STRING
|
||||
final_loss:
|
||||
parameterType: NUMBER_DOUBLE
|
||||
model_dir:
|
||||
parameterType: STRING
|
||||
comp-transcribe-and-diarise:
|
||||
executorLabel: exec-transcribe-and-diarise
|
||||
inputDefinitions:
|
||||
parameters:
|
||||
s3_bucket:
|
||||
parameterType: STRING
|
||||
s3_endpoint:
|
||||
parameterType: STRING
|
||||
s3_key:
|
||||
parameterType: STRING
|
||||
whisper_url:
|
||||
defaultValue: http://ai-inference-serve-svc.kuberay.svc.cluster.local:8000/whisper
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
outputDefinitions:
|
||||
parameters:
|
||||
audio_path:
|
||||
parameterType: STRING
|
||||
speakers:
|
||||
parameterType: STRING
|
||||
transcript_json:
|
||||
parameterType: STRING
|
||||
deploymentSpec:
|
||||
executors:
|
||||
exec-extract-speaker-segments:
|
||||
container:
|
||||
args:
|
||||
- --executor_input
|
||||
- '{{$}}'
|
||||
- --function_to_execute
|
||||
- extract_speaker_segments
|
||||
command:
|
||||
- sh
|
||||
- -c
|
||||
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
|
||||
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
|
||||
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
|
||||
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' && \"\
|
||||
$0\" \"$@\"\n"
|
||||
- sh
|
||||
- -ec
|
||||
- 'program_path=$(mktemp -d)
|
||||
|
||||
|
||||
printf "%s" "$0" > "$program_path/ephemeral_component.py"
|
||||
|
||||
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
|
||||
|
||||
'
|
||||
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
|
||||
\ *\n\ndef extract_speaker_segments(\n transcript_json: str,\n audio_path:\
|
||||
\ str,\n target_speaker: str,\n min_duration_s: float = 1.0,\n \
|
||||
\ max_duration_s: float = 15.0,\n) -> NamedTuple(\"SpeakerSegments\", [(\"\
|
||||
segments_json\", str), (\"num_segments\", int), (\"total_duration_s\", float)]):\n\
|
||||
\ \"\"\"Slice the audio into per-utterance WAV files for the target speaker.\"\
|
||||
\"\"\n import json\n import os\n import subprocess\n import\
|
||||
\ tempfile\n\n out = NamedTuple(\"SpeakerSegments\", [(\"segments_json\"\
|
||||
, str), (\"num_segments\", int), (\"total_duration_s\", float)])\n work\
|
||||
\ = tempfile.mkdtemp()\n wavs_dir = os.path.join(work, \"wavs\")\n \
|
||||
\ os.makedirs(wavs_dir, exist_ok=True)\n\n # Install ffmpeg\n subprocess.run([\"\
|
||||
apt-get\", \"update\", \"-qq\"], capture_output=True)\n subprocess.run([\"\
|
||||
apt-get\", \"install\", \"-y\", \"-qq\", \"ffmpeg\"], capture_output=True,\
|
||||
\ check=True)\n\n segments = json.loads(transcript_json)\n\n # Filter\
|
||||
\ by speaker \u2014 fuzzy match (case-insensitive, partial)\n target_lower\
|
||||
\ = target_speaker.lower()\n matched = []\n for seg in segments:\n\
|
||||
\ spk = seg.get(\"speaker\", \"\").lower()\n if target_lower\
|
||||
\ in spk or spk in target_lower:\n matched.append(seg)\n\n \
|
||||
\ # If no speaker labels matched, the user may have given a name\n #\
|
||||
\ that doesn't appear. Fall back to using ALL segments.\n if not matched:\n\
|
||||
\ print(f\"WARNING: No segments matched speaker '{target_speaker}'.\
|
||||
\ \"\n f\"Using all {len(segments)} segments.\")\n matched\
|
||||
\ = segments\n\n print(f\"Matched {len(matched)} segments for speaker\
|
||||
\ '{target_speaker}'\")\n\n kept = []\n total_dur = 0.0\n for i,\
|
||||
\ seg in enumerate(matched):\n start = float(seg.get(\"start\", 0))\n\
|
||||
\ end = float(seg.get(\"end\", start + 5))\n duration = end\
|
||||
\ - start\n text = seg.get(\"text\", \"\").strip()\n\n if\
|
||||
\ duration < min_duration_s or not text:\n continue\n \
|
||||
\ if duration > max_duration_s:\n end = start + max_duration_s\n\
|
||||
\ duration = max_duration_s\n\n wav_name = f\"utt_{i:04d}.wav\"\
|
||||
\n wav_out = os.path.join(wavs_dir, wav_name)\n subprocess.run(\n\
|
||||
\ [\"ffmpeg\", \"-y\", \"-i\", audio_path,\n \"-ss\"\
|
||||
, str(start), \"-to\", str(end),\n \"-ac\", \"1\", \"-ar\",\
|
||||
\ \"22050\", \"-sample_fmt\", \"s16\",\n wav_out],\n \
|
||||
\ capture_output=True, check=True,\n )\n\n kept.append({\n\
|
||||
\ \"wav\": wav_name,\n \"text\": text,\n \
|
||||
\ \"start\": start,\n \"end\": end,\n \"duration\"\
|
||||
: round(duration, 2),\n })\n total_dur += duration\n\n \
|
||||
\ print(f\"Extracted {len(kept)} utterances, total {total_dur:.1f}s\")\n\
|
||||
\n return out(\n segments_json=json.dumps({\"wavs_dir\": wavs_dir,\
|
||||
\ \"utterances\": kept}),\n num_segments=len(kept),\n total_duration_s=round(total_dur,\
|
||||
\ 2),\n )\n\n"
|
||||
image: python:3.13-slim
|
||||
exec-log-training-metrics:
|
||||
container:
|
||||
args:
|
||||
- --executor_input
|
||||
- '{{$}}'
|
||||
- --function_to_execute
|
||||
- log_training_metrics
|
||||
command:
|
||||
- sh
|
||||
- -c
|
||||
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
|
||||
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
|
||||
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
|
||||
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
|
||||
\ python3 -m pip install --quiet --no-warn-script-location 'mlflow>=2.10.0'\
|
||||
\ 'requests' && \"$0\" \"$@\"\n"
|
||||
- sh
|
||||
- -ec
|
||||
- 'program_path=$(mktemp -d)
|
||||
|
||||
|
||||
printf "%s" "$0" > "$program_path/ephemeral_component.py"
|
||||
|
||||
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
|
||||
|
||||
'
|
||||
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
|
||||
\ *\n\ndef log_training_metrics(\n voice_name: str,\n num_segments:\
|
||||
\ int,\n total_duration_s: float,\n final_loss: float,\n num_epochs:\
|
||||
\ int,\n batch_size: int,\n learning_rate: float,\n repo_url: str,\n\
|
||||
\ files_pushed: int,\n mlflow_tracking_uri: str = \"http://mlflow.mlflow.svc.cluster.local:80\"\
|
||||
,\n experiment_name: str = \"voice-cloning\",\n) -> NamedTuple(\"LogOutput\"\
|
||||
, [(\"run_id\", str)]):\n \"\"\"Log training run to MLflow.\"\"\"\n \
|
||||
\ import mlflow\n from datetime import datetime\n\n out = NamedTuple(\"\
|
||||
LogOutput\", [(\"run_id\", str)])\n\n mlflow.set_tracking_uri(mlflow_tracking_uri)\n\
|
||||
\ mlflow.set_experiment(experiment_name)\n\n with mlflow.start_run(run_name=f\"\
|
||||
voice-clone-{voice_name}-{datetime.now():%Y%m%d-%H%M}\") as run:\n \
|
||||
\ mlflow.log_params({\n \"voice_name\": voice_name,\n \
|
||||
\ \"base_model\": \"tts_models/en/ljspeech/vits\",\n \"\
|
||||
model_type\": \"coqui-vits\",\n \"num_epochs\": num_epochs,\n\
|
||||
\ \"batch_size\": batch_size,\n \"learning_rate\"\
|
||||
: learning_rate,\n \"sample_rate\": 22050,\n })\n \
|
||||
\ mlflow.log_metrics({\n \"num_training_segments\": num_segments,\n\
|
||||
\ \"total_audio_duration_s\": total_duration_s,\n \
|
||||
\ \"final_loss\": final_loss,\n \"files_pushed\": files_pushed,\n\
|
||||
\ })\n mlflow.set_tags({\n \"pipeline\": \"voice-cloning\"\
|
||||
,\n \"gitea_repo\": repo_url,\n \"voice_name\": voice_name,\n\
|
||||
\ })\n print(f\"Logged to MLflow run: {run.info.run_id}\"\
|
||||
)\n return out(run_id=run.info.run_id)\n\n"
|
||||
image: python:3.13-slim
|
||||
exec-prepare-ljspeech-dataset:
|
||||
container:
|
||||
args:
|
||||
- --executor_input
|
||||
- '{{$}}'
|
||||
- --function_to_execute
|
||||
- prepare_ljspeech_dataset
|
||||
command:
|
||||
- sh
|
||||
- -c
|
||||
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
|
||||
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
|
||||
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
|
||||
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' && \"\
|
||||
$0\" \"$@\"\n"
|
||||
- sh
|
||||
- -ec
|
||||
- 'program_path=$(mktemp -d)
|
||||
|
||||
|
||||
printf "%s" "$0" > "$program_path/ephemeral_component.py"
|
||||
|
||||
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
|
||||
|
||||
'
|
||||
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
|
||||
\ *\n\ndef prepare_ljspeech_dataset(\n segments_json: str,\n voice_name:\
|
||||
\ str,\n language: str = \"en\",\n) -> NamedTuple(\"DatasetOutput\",\
|
||||
\ [(\"dataset_dir\", str), (\"num_samples\", int)]):\n \"\"\"Create metadata.csv\
|
||||
\ + wavs/ in LJSpeech format.\"\"\"\n import json\n import os\n \
|
||||
\ import shutil\n\n out = NamedTuple(\"DatasetOutput\", [(\"dataset_dir\"\
|
||||
, str), (\"num_samples\", int)])\n\n data = json.loads(segments_json)\n\
|
||||
\ wavs_src = data[\"wavs_dir\"]\n utterances = data[\"utterances\"\
|
||||
]\n\n dataset_dir = os.path.join(os.path.dirname(wavs_src), \"dataset\"\
|
||||
)\n wavs_dst = os.path.join(dataset_dir, \"wavs\")\n os.makedirs(wavs_dst,\
|
||||
\ exist_ok=True)\n\n lines = []\n for utt in utterances:\n \
|
||||
\ src = os.path.join(wavs_src, utt[\"wav\"])\n dst = os.path.join(wavs_dst,\
|
||||
\ utt[\"wav\"])\n shutil.copy2(src, dst)\n stem = os.path.splitext(utt[\"\
|
||||
wav\"])[0]\n # LJSpeech format: id|text|text\n text = utt[\"\
|
||||
text\"].replace(\"|\", \" \")\n lines.append(f\"{stem}|{text}|{text}\"\
|
||||
)\n\n metadata_path = os.path.join(dataset_dir, \"metadata.csv\")\n \
|
||||
\ with open(metadata_path, \"w\", encoding=\"utf-8\") as f:\n f.write(\"\
|
||||
\\n\".join(lines))\n\n # Dataset config for reference\n import json\
|
||||
\ as _json\n config = {\n \"name\": voice_name,\n \"language\"\
|
||||
: language,\n \"num_samples\": len(lines),\n \"format\": \"\
|
||||
ljspeech\",\n \"sample_rate\": 22050,\n }\n with open(os.path.join(dataset_dir,\
|
||||
\ \"dataset_config.json\"), \"w\") as f:\n _json.dump(config, f,\
|
||||
\ indent=2)\n\n print(f\"LJSpeech dataset ready: {len(lines)} samples\"\
|
||||
)\n return out(dataset_dir=dataset_dir, num_samples=len(lines))\n\n"
|
||||
image: python:3.13-slim
|
||||
exec-push-model-to-gitea:
|
||||
container:
|
||||
args:
|
||||
- --executor_input
|
||||
- '{{$}}'
|
||||
- --function_to_execute
|
||||
- push_model_to_gitea
|
||||
command:
|
||||
- sh
|
||||
- -c
|
||||
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
|
||||
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
|
||||
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
|
||||
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
|
||||
\ python3 -m pip install --quiet --no-warn-script-location 'requests' &&\
|
||||
\ \"$0\" \"$@\"\n"
|
||||
- sh
|
||||
- -ec
|
||||
- 'program_path=$(mktemp -d)
|
||||
|
||||
|
||||
printf "%s" "$0" > "$program_path/ephemeral_component.py"
|
||||
|
||||
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
|
||||
|
||||
'
|
||||
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
|
||||
\ *\n\ndef push_model_to_gitea(\n model_dir: str,\n voice_name: str,\n\
|
||||
\ gitea_url: str = \"http://gitea-http.gitea.svc.cluster.local:3000\"\
|
||||
,\n gitea_owner: str = \"daviestechlabs\",\n gitea_repo: str = \"\
|
||||
voice-models\",\n gitea_username: str = \"\",\n gitea_password: str\
|
||||
\ = \"\",\n) -> NamedTuple(\"PushOutput\", [(\"repo_url\", str), (\"files_pushed\"\
|
||||
, int)]):\n \"\"\"Package and push the trained model to a Gitea repository.\"\
|
||||
\"\"\n import base64\n import glob\n import json\n import os\n\
|
||||
\ import requests\n\n out = NamedTuple(\"PushOutput\", [(\"repo_url\"\
|
||||
, str), (\"files_pushed\", int)])\n session = requests.Session()\n \
|
||||
\ session.auth = (gitea_username, gitea_password) if gitea_username else\
|
||||
\ None\n\n api = f\"{gitea_url}/api/v1\"\n repo_url = f\"{gitea_url}/{gitea_owner}/{gitea_repo}\"\
|
||||
\n\n # \u2500\u2500 Ensure repo exists \u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n r = session.get(f\"{api}/repos/{gitea_owner}/{gitea_repo}\"\
|
||||
, timeout=30)\n if r.status_code == 404:\n print(f\"Creating repository:\
|
||||
\ {gitea_owner}/{gitea_repo}\")\n r = session.post(\n \
|
||||
\ f\"{api}/orgs/{gitea_owner}/repos\",\n json={\n \
|
||||
\ \"name\": gitea_repo,\n \"description\": \"Trained\
|
||||
\ voice models from voice cloning pipeline\",\n \"private\"\
|
||||
: False,\n \"auto_init\": True,\n },\n \
|
||||
\ timeout=30,\n )\n if r.status_code not in (200, 201):\n\
|
||||
\ r = session.post(\n f\"{api}/user/repos\",\n\
|
||||
\ json={\"name\": gitea_repo, \"description\": \"Trained\
|
||||
\ voice models\", \"auto_init\": True},\n timeout=30,\n \
|
||||
\ )\n r.raise_for_status()\n print(\"Repository\
|
||||
\ created\")\n\n # \u2500\u2500 Collect model files \u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n files_to_push = []\n\
|
||||
\n # Best model checkpoint\n for pattern in [\"**/best_model*.pth\"\
|
||||
, \"**/*.pth\"]:\n found = glob.glob(os.path.join(model_dir, pattern),\
|
||||
\ recursive=True)\n if found:\n found.sort(key=os.path.getmtime,\
|
||||
\ reverse=True)\n files_to_push.append(found[0])\n \
|
||||
\ break\n\n # Config\n for pattern in [\"**/config.json\"]:\n \
|
||||
\ found = glob.glob(os.path.join(model_dir, pattern), recursive=True)\n\
|
||||
\ if found:\n files_to_push.append(found[0])\n\n #\
|
||||
\ Model info\n model_info = {\n \"name\": voice_name,\n \
|
||||
\ \"type\": \"coqui-vits\",\n \"base_model\": \"tts_models/en/ljspeech/vits\"\
|
||||
,\n \"sample_rate\": 22050,\n }\n info_path = os.path.join(model_dir,\
|
||||
\ \"model_info.json\")\n with open(info_path, \"w\") as f:\n json.dump(model_info,\
|
||||
\ f, indent=2)\n files_to_push.append(info_path)\n\n # \u2500\u2500\
|
||||
\ Push each file \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\n pushed = 0\n for fpath in files_to_push:\n\
|
||||
\ rel = os.path.relpath(fpath, model_dir)\n gitea_path = f\"\
|
||||
{voice_name}/{rel}\"\n print(f\"Pushing: {gitea_path} ({os.path.getsize(fpath)}\
|
||||
\ bytes)\")\n\n with open(fpath, \"rb\") as f:\n content_b64\
|
||||
\ = base64.b64encode(f.read()).decode()\n\n # Check if file exists\n\
|
||||
\ r = session.get(\n f\"{api}/repos/{gitea_owner}/{gitea_repo}/contents/{gitea_path}\"\
|
||||
,\n timeout=30,\n )\n\n payload = {\n \
|
||||
\ \"content\": content_b64,\n \"message\": f\"Upload {voice_name}:\
|
||||
\ {rel}\",\n }\n\n if r.status_code == 200:\n sha\
|
||||
\ = r.json().get(\"sha\", \"\")\n payload[\"sha\"] = sha\n \
|
||||
\ r = session.put(\n f\"{api}/repos/{gitea_owner}/{gitea_repo}/contents/{gitea_path}\"\
|
||||
,\n json=payload, timeout=120,\n )\n else:\n\
|
||||
\ r = session.post(\n f\"{api}/repos/{gitea_owner}/{gitea_repo}/contents/{gitea_path}\"\
|
||||
,\n json=payload, timeout=120,\n )\n\n \
|
||||
\ if r.status_code in (200, 201):\n pushed += 1\n \
|
||||
\ print(f\" \u2713 Pushed\")\n else:\n print(f\" \u2717\
|
||||
\ Failed ({r.status_code}): {r.text[:200]}\")\n\n print(f\"\\nPushed\
|
||||
\ {pushed}/{len(files_to_push)} files to {repo_url}\")\n return out(repo_url=repo_url,\
|
||||
\ files_pushed=pushed)\n\n"
|
||||
image: python:3.13-slim
|
||||
exec-train-vits-voice:
|
||||
container:
|
||||
args:
|
||||
- --executor_input
|
||||
- '{{$}}'
|
||||
- --function_to_execute
|
||||
- train_vits_voice
|
||||
command:
|
||||
- sh
|
||||
- -c
|
||||
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
|
||||
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
|
||||
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
|
||||
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' && \"\
|
||||
$0\" \"$@\"\n"
|
||||
- sh
|
||||
- -ec
|
||||
- 'program_path=$(mktemp -d)
|
||||
|
||||
|
||||
printf "%s" "$0" > "$program_path/ephemeral_component.py"
|
||||
|
||||
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
|
||||
|
||||
'
|
||||
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
|
||||
\ *\n\ndef train_vits_voice(\n dataset_dir: str,\n voice_name: str,\n\
|
||||
\ language: str = \"en\",\n base_model: str = \"tts_models/en/ljspeech/vits\"\
|
||||
,\n num_epochs: int = 100,\n batch_size: int = 16,\n learning_rate:\
|
||||
\ float = 0.0001,\n) -> NamedTuple(\"TrainOutput\", [(\"model_dir\", str),\
|
||||
\ (\"best_checkpoint\", str), (\"final_loss\", float)]):\n \"\"\"Fine-tune\
|
||||
\ a VITS model on the speaker dataset.\"\"\"\n import os\n import\
|
||||
\ json\n import glob\n\n out = NamedTuple(\"TrainOutput\", [(\"model_dir\"\
|
||||
, str), (\"best_checkpoint\", str), (\"final_loss\", float)])\n\n OUTPUT_DIR\
|
||||
\ = \"/tmp/vits_output\"\n os.makedirs(OUTPUT_DIR, exist_ok=True)\n\n\
|
||||
\ print(f\"=== Coqui VITS Voice Training ===\")\n print(f\"Voice name\
|
||||
\ : {voice_name}\")\n print(f\"Base model : {base_model}\")\n print(f\"\
|
||||
Dataset : {dataset_dir}\")\n print(f\"Epochs : {num_epochs}\"\
|
||||
)\n print(f\"Batch size : {batch_size}\")\n print(f\"LR :\
|
||||
\ {learning_rate}\")\n\n # \u2500\u2500 Download base model checkpoint\
|
||||
\ \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\n restore_path = None\n if base_model and base_model != \"none\"\
|
||||
:\n from TTS.utils.manage import ModelManager\n manager =\
|
||||
\ ModelManager()\n model_path, config_path, _ = manager.download_model(base_model)\n\
|
||||
\ restore_path = model_path\n print(f\"Base model checkpoint:\
|
||||
\ {restore_path}\")\n\n # \u2500\u2500 Configure and train \u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n from trainer\
|
||||
\ import Trainer, TrainerArgs\n from TTS.tts.configs.vits_config import\
|
||||
\ VitsConfig\n from TTS.tts.configs.shared_configs import BaseDatasetConfig\n\
|
||||
\ from TTS.tts.datasets import load_tts_samples\n from TTS.tts.models.vits\
|
||||
\ import Vits\n from TTS.tts.utils.text.tokenizer import TTSTokenizer\n\
|
||||
\ from TTS.utils.audio import AudioProcessor\n\n dataset_config =\
|
||||
\ BaseDatasetConfig(\n formatter=\"ljspeech\",\n meta_file_train=\"\
|
||||
metadata.csv\",\n path=dataset_dir,\n language=language,\n\
|
||||
\ )\n\n config = VitsConfig(\n run_name=voice_name,\n \
|
||||
\ output_path=OUTPUT_DIR,\n datasets=[dataset_config],\n \
|
||||
\ batch_size=batch_size,\n eval_batch_size=max(1, batch_size //\
|
||||
\ 2),\n num_loader_workers=4,\n num_eval_loader_workers=2,\n\
|
||||
\ run_eval=True,\n test_delay_epochs=5,\n epochs=num_epochs,\n\
|
||||
\ text_cleaner=\"phoneme_cleaners\",\n use_phonemes=True,\n\
|
||||
\ phoneme_language=language,\n phoneme_cache_path=os.path.join(OUTPUT_DIR,\
|
||||
\ \"phoneme_cache\"),\n compute_input_seq_cache=True,\n print_step=25,\n\
|
||||
\ print_eval=False,\n mixed_precision=True,\n save_step=500,\n\
|
||||
\ save_n_checkpoints=3,\n save_best_after=1000,\n lr=learning_rate,\n\
|
||||
\ audio={\n \"sample_rate\": 22050,\n \"resample\"\
|
||||
: True,\n \"do_trim_silence\": True,\n \"trim_db\"\
|
||||
: 45,\n },\n )\n\n ap = AudioProcessor.init_from_config(config)\n\
|
||||
\ tokenizer, config = TTSTokenizer.init_from_config(config)\n\n train_samples,\
|
||||
\ eval_samples = load_tts_samples(\n dataset_config,\n eval_split=True,\n\
|
||||
\ eval_split_max_size=config.eval_split_max_size,\n eval_split_size=config.eval_split_size,\n\
|
||||
\ )\n print(f\"Training samples: {len(train_samples)}\")\n print(f\"\
|
||||
Eval samples: {len(eval_samples)}\")\n\n model = Vits(config, ap,\
|
||||
\ tokenizer, speaker_manager=None)\n\n trainer_args = TrainerArgs(\n\
|
||||
\ restore_path=restore_path,\n skip_train_epoch=False,\n \
|
||||
\ )\n\n trainer = Trainer(\n trainer_args,\n config,\n\
|
||||
\ output_path=OUTPUT_DIR,\n model=model,\n train_samples=train_samples,\n\
|
||||
\ eval_samples=eval_samples,\n )\n\n trainer.fit()\n\n #\
|
||||
\ \u2500\u2500 Find best checkpoint \u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\n best_files = glob.glob(os.path.join(OUTPUT_DIR,\
|
||||
\ \"**/best_model*.pth\"), recursive=True)\n if not best_files:\n \
|
||||
\ best_files = glob.glob(os.path.join(OUTPUT_DIR, \"**/*.pth\"), recursive=True)\n\
|
||||
\ best_files.sort(key=os.path.getmtime, reverse=True)\n best_checkpoint\
|
||||
\ = best_files[0] if best_files else \"\"\n\n # Try to read final loss\
|
||||
\ from trainer\n final_loss = 0.0\n try:\n final_loss = float(trainer.keep_avg_train[\"\
|
||||
avg_loss\"])\n except Exception:\n pass\n\n print(f\"Training\
|
||||
\ complete. Best checkpoint: {best_checkpoint}\")\n print(f\"Final loss:\
|
||||
\ {final_loss:.4f}\")\n\n return out(model_dir=OUTPUT_DIR, best_checkpoint=best_checkpoint,\
|
||||
\ final_loss=final_loss)\n\n"
|
||||
image: ghcr.io/coqui-ai/tts:latest
|
||||
resources:
|
||||
accelerator:
|
||||
resourceCount: '1'
|
||||
resourceType: gpu
|
||||
resourceCpuLimit: '8'
|
||||
resourceCpuRequest: '4'
|
||||
resourceMemoryLimit: 32Gi
|
||||
resourceMemoryRequest: 16Gi
|
||||
exec-transcribe-and-diarise:
|
||||
container:
|
||||
args:
|
||||
- --executor_input
|
||||
- '{{$}}'
|
||||
- --function_to_execute
|
||||
- transcribe_and_diarise
|
||||
command:
|
||||
- sh
|
||||
- -c
|
||||
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
|
||||
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
|
||||
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
|
||||
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
|
||||
\ python3 -m pip install --quiet --no-warn-script-location 'requests' 'boto3'\
|
||||
\ && \"$0\" \"$@\"\n"
|
||||
- sh
|
||||
- -ec
|
||||
- 'program_path=$(mktemp -d)
|
||||
|
||||
|
||||
printf "%s" "$0" > "$program_path/ephemeral_component.py"
|
||||
|
||||
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
|
||||
|
||||
'
|
||||
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
|
||||
\ *\n\ndef transcribe_and_diarise(\n s3_endpoint: str,\n s3_bucket:\
|
||||
\ str,\n s3_key: str,\n whisper_url: str = \"http://ai-inference-serve-svc.kuberay.svc.cluster.local:8000/whisper\"\
|
||||
,\n) -> NamedTuple(\"TranscriptOutput\", [(\"transcript_json\", str), (\"\
|
||||
speakers\", str), (\"audio_path\", str)]):\n \"\"\"Download audio from\
|
||||
\ Quobjects S3, transcribe via Whisper with timestamps.\"\"\"\n import\
|
||||
\ json\n import os\n import subprocess\n import tempfile\n import\
|
||||
\ base64\n import boto3\n import requests\n\n out = NamedTuple(\"\
|
||||
TranscriptOutput\", [(\"transcript_json\", str), (\"speakers\", str), (\"\
|
||||
audio_path\", str)])\n work = tempfile.mkdtemp()\n\n # \u2500\u2500\
|
||||
\ Download audio from Quobjects S3 \u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\n ext = os.path.splitext(s3_key)[-1] or \".wav\"\n audio_path\
|
||||
\ = os.path.join(work, f\"audio_raw{ext}\")\n\n client = boto3.client(\n\
|
||||
\ \"s3\",\n endpoint_url=f\"http://{s3_endpoint}\",\n \
|
||||
\ aws_access_key_id=\"\",\n aws_secret_access_key=\"\",\n \
|
||||
\ config=boto3.session.Config(signature_version=\"UNSIGNED\"),\n )\n\
|
||||
\ print(f\"Downloading s3://{s3_bucket}/{s3_key} from {s3_endpoint}\"\
|
||||
)\n client.download_file(s3_bucket, s3_key, audio_path)\n print(f\"\
|
||||
Downloaded {os.path.getsize(audio_path)} bytes\")\n\n # \u2500\u2500\
|
||||
\ Normalise to 16 kHz mono WAV \u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
||||
\u2500\u2500\u2500\u2500\u2500\u2500\n wav_path = os.path.join(work,\
|
||||
\ \"audio.wav\")\n subprocess.run(\n [\"apt-get\", \"update\"\
|
||||
, \"-qq\"],\n capture_output=True,\n )\n subprocess.run(\n\
|
||||
\ [\"apt-get\", \"install\", \"-y\", \"-qq\", \"ffmpeg\"],\n \
|
||||
\ capture_output=True, check=True,\n )\n subprocess.run(\n \
|
||||
\ [\"ffmpeg\", \"-y\", \"-i\", audio_path, \"-ac\", \"1\",\n \
|
||||
\ \"-ar\", \"16000\", \"-sample_fmt\", \"s16\", wav_path],\n capture_output=True,\
|
||||
\ check=True,\n )\n\n # \u2500\u2500 Send to Whisper for timestamped\
|
||||
\ transcription \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n\
|
||||
\ with open(wav_path, \"rb\") as f:\n audio_b64 = base64.b64encode(f.read()).decode()\n\
|
||||
\n payload = {\n \"audio\": audio_b64,\n \"response_format\"\
|
||||
: \"verbose_json\",\n \"timestamp_granularities\": [\"segment\"],\n\
|
||||
\ }\n resp = requests.post(whisper_url, json=payload, timeout=600)\n\
|
||||
\ resp.raise_for_status()\n result = resp.json()\n\n segments =\
|
||||
\ result.get(\"segments\", [])\n print(f\"Whisper returned {len(segments)}\
|
||||
\ segments\")\n\n # \u2500\u2500 Group segments by speaker if diarisation\
|
||||
\ is present \u2500\u2500\u2500\n # Whisper may not diarise, but we still\
|
||||
\ produce segments with\n # start/end timestamps that the next step can\
|
||||
\ use.\n speakers = set()\n for i, seg in enumerate(segments):\n \
|
||||
\ spk = seg.get(\"speaker\", f\"SPEAKER_{i // 10}\")\n seg[\"\
|
||||
speaker\"] = spk\n speakers.add(spk)\n\n speakers_list = sorted(speakers)\n\
|
||||
\ print(f\"Detected speakers: {speakers_list}\")\n\n return out(\n\
|
||||
\ transcript_json=json.dumps(segments),\n speakers=json.dumps(speakers_list),\n\
|
||||
\ audio_path=wav_path,\n )\n\n"
|
||||
image: python:3.13-slim
|
||||
pipelineInfo:
|
||||
description: Extract a speaker from audio+transcript, fine-tune a Coqui VITS voice
|
||||
model, push to Gitea, and log metrics to MLflow.
|
||||
name: voice-cloning-pipeline
|
||||
root:
|
||||
dag:
|
||||
tasks:
|
||||
extract-speaker-segments:
|
||||
cachingOptions:
|
||||
enableCache: true
|
||||
componentRef:
|
||||
name: comp-extract-speaker-segments
|
||||
dependentTasks:
|
||||
- transcribe-and-diarise
|
||||
inputs:
|
||||
parameters:
|
||||
audio_path:
|
||||
taskOutputParameter:
|
||||
outputParameterKey: audio_path
|
||||
producerTask: transcribe-and-diarise
|
||||
max_duration_s:
|
||||
componentInputParameter: max_segment_duration_s
|
||||
min_duration_s:
|
||||
componentInputParameter: min_segment_duration_s
|
||||
target_speaker:
|
||||
componentInputParameter: target_speaker
|
||||
transcript_json:
|
||||
taskOutputParameter:
|
||||
outputParameterKey: transcript_json
|
||||
producerTask: transcribe-and-diarise
|
||||
taskInfo:
|
||||
name: extract-speaker-segments
|
||||
log-training-metrics:
|
||||
cachingOptions:
|
||||
enableCache: true
|
||||
componentRef:
|
||||
name: comp-log-training-metrics
|
||||
dependentTasks:
|
||||
- extract-speaker-segments
|
||||
- push-model-to-gitea
|
||||
- train-vits-voice
|
||||
inputs:
|
||||
parameters:
|
||||
batch_size:
|
||||
componentInputParameter: batch_size
|
||||
files_pushed:
|
||||
taskOutputParameter:
|
||||
outputParameterKey: files_pushed
|
||||
producerTask: push-model-to-gitea
|
||||
final_loss:
|
||||
taskOutputParameter:
|
||||
outputParameterKey: final_loss
|
||||
producerTask: train-vits-voice
|
||||
learning_rate:
|
||||
componentInputParameter: learning_rate
|
||||
mlflow_tracking_uri:
|
||||
componentInputParameter: mlflow_tracking_uri
|
||||
num_epochs:
|
||||
componentInputParameter: num_epochs
|
||||
num_segments:
|
||||
taskOutputParameter:
|
||||
outputParameterKey: num_segments
|
||||
producerTask: extract-speaker-segments
|
||||
repo_url:
|
||||
taskOutputParameter:
|
||||
outputParameterKey: repo_url
|
||||
producerTask: push-model-to-gitea
|
||||
total_duration_s:
|
||||
taskOutputParameter:
|
||||
outputParameterKey: total_duration_s
|
||||
producerTask: extract-speaker-segments
|
||||
voice_name:
|
||||
componentInputParameter: voice_name
|
||||
taskInfo:
|
||||
name: log-training-metrics
|
||||
prepare-ljspeech-dataset:
|
||||
cachingOptions:
|
||||
enableCache: true
|
||||
componentRef:
|
||||
name: comp-prepare-ljspeech-dataset
|
||||
dependentTasks:
|
||||
- extract-speaker-segments
|
||||
inputs:
|
||||
parameters:
|
||||
language:
|
||||
componentInputParameter: language
|
||||
segments_json:
|
||||
taskOutputParameter:
|
||||
outputParameterKey: segments_json
|
||||
producerTask: extract-speaker-segments
|
||||
voice_name:
|
||||
componentInputParameter: voice_name
|
||||
taskInfo:
|
||||
name: prepare-ljspeech-dataset
|
||||
push-model-to-gitea:
|
||||
cachingOptions:
|
||||
enableCache: true
|
||||
componentRef:
|
||||
name: comp-push-model-to-gitea
|
||||
dependentTasks:
|
||||
- train-vits-voice
|
||||
inputs:
|
||||
parameters:
|
||||
gitea_owner:
|
||||
componentInputParameter: gitea_owner
|
||||
gitea_password:
|
||||
componentInputParameter: gitea_password
|
||||
gitea_repo:
|
||||
componentInputParameter: gitea_repo
|
||||
gitea_url:
|
||||
componentInputParameter: gitea_url
|
||||
gitea_username:
|
||||
componentInputParameter: gitea_username
|
||||
model_dir:
|
||||
taskOutputParameter:
|
||||
outputParameterKey: model_dir
|
||||
producerTask: train-vits-voice
|
||||
voice_name:
|
||||
componentInputParameter: voice_name
|
||||
taskInfo:
|
||||
name: push-model-to-gitea
|
||||
train-vits-voice:
|
||||
cachingOptions:
|
||||
enableCache: true
|
||||
componentRef:
|
||||
name: comp-train-vits-voice
|
||||
dependentTasks:
|
||||
- prepare-ljspeech-dataset
|
||||
inputs:
|
||||
parameters:
|
||||
base_model:
|
||||
componentInputParameter: base_model
|
||||
batch_size:
|
||||
componentInputParameter: batch_size
|
||||
dataset_dir:
|
||||
taskOutputParameter:
|
||||
outputParameterKey: dataset_dir
|
||||
producerTask: prepare-ljspeech-dataset
|
||||
language:
|
||||
componentInputParameter: language
|
||||
learning_rate:
|
||||
componentInputParameter: learning_rate
|
||||
num_epochs:
|
||||
componentInputParameter: num_epochs
|
||||
voice_name:
|
||||
componentInputParameter: voice_name
|
||||
taskInfo:
|
||||
name: train-vits-voice
|
||||
transcribe-and-diarise:
|
||||
cachingOptions:
|
||||
enableCache: true
|
||||
componentRef:
|
||||
name: comp-transcribe-and-diarise
|
||||
inputs:
|
||||
parameters:
|
||||
s3_bucket:
|
||||
componentInputParameter: s3_bucket
|
||||
s3_endpoint:
|
||||
componentInputParameter: s3_endpoint
|
||||
s3_key:
|
||||
componentInputParameter: s3_key
|
||||
whisper_url:
|
||||
componentInputParameter: whisper_url
|
||||
taskInfo:
|
||||
name: transcribe-and-diarise
|
||||
inputDefinitions:
|
||||
parameters:
|
||||
base_model:
|
||||
defaultValue: tts_models/en/ljspeech/vits
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
batch_size:
|
||||
defaultValue: 16.0
|
||||
isOptional: true
|
||||
parameterType: NUMBER_INTEGER
|
||||
gitea_owner:
|
||||
defaultValue: daviestechlabs
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
gitea_password:
|
||||
defaultValue: ''
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
gitea_repo:
|
||||
defaultValue: voice-models
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
gitea_url:
|
||||
defaultValue: http://gitea-http.gitea.svc.cluster.local:3000
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
gitea_username:
|
||||
defaultValue: ''
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
language:
|
||||
defaultValue: en
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
learning_rate:
|
||||
defaultValue: 0.0001
|
||||
isOptional: true
|
||||
parameterType: NUMBER_DOUBLE
|
||||
max_segment_duration_s:
|
||||
defaultValue: 15.0
|
||||
isOptional: true
|
||||
parameterType: NUMBER_DOUBLE
|
||||
min_segment_duration_s:
|
||||
defaultValue: 1.0
|
||||
isOptional: true
|
||||
parameterType: NUMBER_DOUBLE
|
||||
mlflow_tracking_uri:
|
||||
defaultValue: http://mlflow.mlflow.svc.cluster.local:80
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
num_epochs:
|
||||
defaultValue: 100.0
|
||||
isOptional: true
|
||||
parameterType: NUMBER_INTEGER
|
||||
s3_bucket:
|
||||
defaultValue: training-data
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
s3_endpoint:
|
||||
defaultValue: candlekeep.lab.daviestechlabs.io
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
s3_key:
|
||||
defaultValue: ''
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
target_speaker:
|
||||
defaultValue: SPEAKER_0
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
voice_name:
|
||||
defaultValue: custom-voice
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
whisper_url:
|
||||
defaultValue: http://ai-inference-serve-svc.kuberay.svc.cluster.local:8000/whisper
|
||||
isOptional: true
|
||||
parameterType: STRING
|
||||
schemaVersion: 2.1.0
|
||||
sdkVersion: kfp-2.12.1
|
||||
Reference in New Issue
Block a user