Initial commit

0_transcode.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+
+
+# Chose one of the following bitrates, depending on the model you want to train
+# x-low, low models
+#RATE=16000
+# medium, high models
+RATE=22050
+
+
+CODEC=pcm_s16le
+CHANNELS=1
+
+TMP=/tmp/wav
+mkdir -p ${TMP}
+
+for file in ./raw/*.wav; do
+  ffmpeg -i ${file} -ac ${CHANNELS} -acodec ${CODEC} -ar ${RATE} -y ${TMP}/$(basename ${file})
+done
+
+rm ./raw/*.wav
+mv ${TMP}/*.wav ./raw/

1_from_good.py

@@ -0,0 +1,25 @@
+#!/usr/bin/env python3
+
+import os
+import shutil
+import os
+from pathlib import Path
+
+
+IN="./raw"
+OUT="./raw_good"
+
+
+GOOD = None
+with open('./good.txt', 'r') as f:
+    GOOD = list(map(str.strip, f.readlines()))
+
+wav_files = []
+for root, _, files in os.walk(IN):
+    for file in files:
+        if not (file.endswith('.wav') and file[:-4] in GOOD): continue
+        src = Path(os.path.join(root, file))
+        dst_name = Path(OUT) / src.name
+        shutil.move(src, dst_name)
+
+

1_sort_good_bad.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python3
+
+# This script helps you sorting good-quality voice-lines from low-quality for training.
+# It plays the voice lines in *TWICE THE SPEED* to speed up this process (still takes hours) - This will make the voices sound like chipmunk voices. That is fine.
+# Have the folowing folders prepared in respect to this repository
+# raw
+#  - p1: Portal1 voice lines
+#  - p2: Portal2 voice lines
+#  - p2dlc: Portal 2 voice lines (if available)
+# raw_good: Destination folder for good voice lines
+# raw_bad: Destination folder for bad voice lines
+
+# Start this program (linux: start a terminal an run it via ./0_sort_good_bad.py)
+# Press left/right *ONCE* to play the first voice line (in twice the speed - aka chipmunk voice)
+# - If it is good: press RIGHT ARROW
+# - If it was bad: press LEFT ARROW
+# - If you want to replay it (or check it without chipmunk effect): press UP arrow
+
+# Get some coffee. This might take a few hours, even though we play the voice lines at twice the speed
+
+import os
+import wave
+import pyaudio
+import tkinter as tk
+import shutil
+import os
+from pathlib import Path
+
+def play_wav_file(file_path, speed = 1):
+    """Play a WAV file."""
+    with wave.open(file_path, 'rb') as wf:
+        p = pyaudio.PyAudio()
+        
+        # Set audio stream parameters
+        stream = p.open(format=p.get_format_from_width(wf.getsampwidth()),
+                        channels=wf.getnchannels(),
+                        rate=int(wf.getframerate() * speed),  # Double the speed
+                        output=True)
+        
+        data = wf.readframes(1024)
+        while data:
+            stream.write(data)
+            data = wf.readframes(1024)
+        
+        stream.stop_stream()
+        stream.close()
+        p.terminate()
+
+def find_wav_files(directory):
+    """Recursively find all WAV files in a directory."""
+    wav_files = []
+    for root, _, files in os.walk(directory):
+        for file in files:
+            if file.endswith('.wav'):
+                wav_files.append(os.path.join(root, file))
+    return wav_files
+
+pos = 0
+files = []
+wav = ""
+def play(speed = 1.5):
+    global pos, files, wav
+    wav = files[pos]
+    print(f"File: {wav}")
+    play_wav_file(wav, speed)
+    pos += 1
+    print("---> ?")
+
+def move(src, dst):
+    src = Path(src)
+    #dst_name = Path(dst) / f"{src.parent.name}_{src.name}" # I used to prepepend the parent folders (p1, p2, p2dlc) as I was unsure, if the files have unique names across games. They indeed are unique -> Disabled
+    dst_name = Path(dst) / src.name
+    shutil.move(src, dst_name)
+
+def on_key_press(event):
+    global pos, files, wav
+    if pos >= len(files):
+        print("FINISHED")
+    if pos == 0: return play()
+    speed = 1.5
+    if event.keysym == 'Left':
+        print("No")
+        print("--------------------------------------")
+        move(wav, "raw_bad")
+    elif event.keysym == 'Right':
+        print("Yes")
+        print("--------------------------------------")
+        move(wav, "raw_good")
+    else:
+        speed = 1
+        pos -= 1
+        if pos < 0: pose = 0
+    play(speed)
+
+if __name__ == "__main__":
+    src = "./raw/"
+    files = find_wav_files(src)
+
+    root = tk.Tk()
+    root.title("Left: Discard, Right: Match, Up: Replay normal (no Chipmunk)")
+    root.geometry("300x150")
+    root.bind("<Key>", on_key_press)
+    root.mainloop()

2_transcribe.py

@@ -0,0 +1,46 @@
+#!/usr/bin/env python3
+
+# Inspired by https://blog.networkchuck.com/posts/how-to-clone-a-voice/
+# Enhanced by https://github.com/SYSTRAN/faster-whisper
+
+import os
+from faster_whisper import WhisperModel
+
+# SELECT language and MODEL of the files you want to transcribe
+# I highly suggest to use a large model and to do this on GPU
+LANG="de"
+WHISPER_MODEL = "large-v3"
+
+# Run on GPU
+model = WhisperModel(WHISPER_MODEL, device="cuda", compute_type="float16")
+# or run on CPU with INT8 (will take ages)
+#model = WhisperModel(WHISPER_MODEL, device="cpu", compute_type="int8")
+
+# Path to the directory containing the audio files
+audio_dir = "./raw_good"
+output_csv = "./metadata.csv"
+
+# List all .wav files in the directory
+audio_files = [f for f in os.listdir(audio_dir) if f.endswith(".wav")]
+audio_files.sort()  # Sort the files alphabetically (optional)
+
+# Open the CSV file for writing
+with open(output_csv, "w") as f:
+    for audio_file in audio_files:
+        # Full path to the audio file
+        audio_path = os.path.join(audio_dir, audio_file)
+
+        segments, info = model.transcribe(audio_path, language=LANG, beam_size=5)
+        
+        transcription = ""
+        for seg in segments:
+            transcription += " "+seg.text.strip()
+        
+        transcription = transcription.strip()
+
+        # Write the filename (without .wav extension) and transcription to the CSV
+        file_id = os.path.splitext(audio_file)[0]  # Get file name without extension
+        f.write(f"{file_id}|{transcription}\n")
+        print(f"{file_id}|{transcription}")
+
+print(f"Transcriptions complete! Metadata saved to {output_csv}")

3_gen_traindata.sh

@@ -0,0 +1,12 @@
+#!/bin/bash
+
+LANG=de
+SAMPLERATE=22050
+
+python3 -m piper_train.preprocess \
+  --language ${LANG} \
+  --input-dir ./raw_good/ \
+  --output-dir ./traindata \
+  --dataset-format ljspeech \
+  --single-speaker \
+  --sample-rate ${SAMPLERATE}

4_train.sh

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+#!/bin/bash
+
+### Training parameters
+# Model to train
+QUALITY=high
+# These two parameters will dictate, how much VRAM will be required
+# NOTE: The following config will require around 20 GB VRAM (high model)
+# Do not lower BATCHSIZE too much, as training will have a lot of "jitter".
+# But reducing PHONEME_MAX too much will discard too long training data
+# However: I was able to train on ~6-7 GB VRAM with BATCHSIZE=12 and PHONEME_MAX=400 (the jitter was clearly visible in tensorboard)
+BATCHSIZE=32
+PHONEME_MAX=400
+
+# Base model to start training from. E.g. here from Thorsten Voice
+BASE_CHKPOINT="./checkpoints/epoch=2665-step=1182078.ckpt"
+
+# Start training from BASE_CHKPOINT
+CHKPOINT=$BASE_CHKPOINT
+
+# CONTINUE from an existing checkpoint.
+# NOTE: It is good practice, to write down the last checkpoint, that was trained after you aborted training.
+# The current checkpoint is automatically printed out by the training process.
+#CHKPOINT="/training/traindata/lightning_logs/version_21/checkpoints/epoch=5661-step=1778926.ckpt"
+
+python3 -m piper_train \
+    --dataset-dir ./traindata \
+    --accelerator 'gpu' \
+    --gpus 1 \
+    --batch-size ${BATCHSIZE} \
+    --validation-split 0.0 \
+    --num-test-examples 0 \
+    --max_epochs 6000 \
+    --resume_from_checkpoint "${CHKPOINT}" \
+    --checkpoint-epochs 1 \
+    --precision 16 \
+    --max-phoneme-ids ${PHONEME_MAX} \
+    --quality ${QUALITY}
+
+    # Batchsize: 32
+    # max-phenomene-ids: 400

5_export.sh

@@ -0,0 +1,11 @@
+#!/usr/bin/bash
+
+NAME=de_DE-glados-high
+
+CHKPOINT="./checkpoints/epoch=5999-step=1838414.ckpt"
+
+python3 -m piper_train.export_onnx \
+    ${CHKPOINT} \
+    ./${NAME}.onnx
+    
+cp ./traindata/config.json ./${NAME}.onnx.json

Dockerfile

@@ -0,0 +1,90 @@
+# Use the official PyTorch image as the base image
+ARG BASE_IMAGE=nvcr.io/nvidia/pytorch:22.03-py3
+ARG PIPER_VERSION=c0670df63daf07070c9be36b5c4bed270ad72383
+ARG PYTHON_VERSION=3.10.13
+ARG PYTHON_BIN=python3.10
+
+########## Build python
+FROM ${BASE_IMAGE} AS pythonbuilder
+ARG PYTHON_VERSION
+
+# Install dependencies needed for building Python
+ENV DEBIAN_FRONTEND  noninteractive
+RUN apt-get update && apt install -y \
+    git build-essential zlib1g-dev libbz2-dev \
+    liblzma-dev libncurses5-dev libreadline6-dev libsqlite3-dev libssl-dev \
+    libgdbm-dev liblzma-dev tk-dev lzma lzma-dev libgdbm-dev libffi-dev
+
+RUN mkdir -pv /src && mkdir -pv /build
+WORKDIR /src
+
+RUN wget https://www.python.org/ftp/python/${PYTHON_VERSION}/Python-${PYTHON_VERSION}.tgz
+RUN tar zxvf Python-${PYTHON_VERSION}.tgz
+
+WORKDIR /src/Python-${PYTHON_VERSION}
+# Prefix is not only setting the destination where "make altinstall" puts the files, but also compiles in certain path such, that any program
+# that will build against this python version, expect header files etc to be there -> We install into a clean /usr/local and then move the install files to /build
+RUN ./configure --enable-optimizations --prefix=/usr/local
+RUN make -j8
+
+# Make clean destination which we then copy over to the actual container
+RUN rm -rf /usr/local && mkdir -pv /usr/local
+RUN make altinstall
+
+RUN mv /usr/local/* /build
+
+
+
+
+########## Build piper-train
+FROM ${BASE_IMAGE}
+ARG PIPER_VERSION
+ARG PYTHON_BIN
+
+# Copy python from pythonbuilder stage
+RUN mkdir -pv /usr/local/
+COPY --from=pythonbuilder /build/ /usr/local
+
+# Set environment variables for Numba cache directory
+ENV NUMBA_CACHE_DIR=.numba_cache
+
+# Install dependencies and tools for training
+ENV DEBIAN_FRONTEND  noninteractive
+RUN apt update && apt install -y \
+    git build-essential espeak-ng ffmpeg && \
+    rm -rf /var/lib/apt/lists/*
+
+# Prepare venv for piper
+RUN /usr/local/bin/${PYTHON_BIN} -m venv /.venv
+# Automatically activate the virtual environment when entering the container via 'docker exec -it <container name> bash'
+RUN echo "source /.venv/bin/activate" >> /etc/bash.bashrc
+
+# Prepare piper
+RUN mkdir -pv /src
+WORKDIR /src
+# 
+RUN git clone https://github.com/rhasspy/piper.git && cd piper && git checkout ${PIPER_VERSION}
+WORKDIR /src/piper/src/python
+
+# Upgrade pip
+RUN source /.venv/bin/activate && pip install "pip<24"
+# Install latest numpy 1.x and tochmetrics 0.x to avoid RTX 4000 issues (https://github.com/rhasspy/piper/issues/295)
+RUN source /.venv/bin/activate && pip install "numpy<2" "torchmetrics<1"
+# Install piper dependencies
+RUN source /.venv/bin/activate && pip install pip wheel setuptools && \
+    pip install -r requirements.txt
+# Build piper-train
+RUN source /.venv/bin/activate && pip install -e . && ./build_monotonic_align.sh
+# Also install piper, in case we want to test current generations
+RUN pip install piper
+
+# Actual training directory: Mount your data folder in here
+RUN mkdir -pv /training
+WORKDIR /training
+
+
+# Makes the container stay up and expose training metrics
+STOPSIGNAL SIGKILL
+CMD tensorboard --logdir .
+
+

README.org

@@ -0,0 +1,115 @@
+* GLaDOS voice model, trained on German Portal 1 and Portal 2 game files
+** Model description
+   This model uses a checkpoint from the [[https://huggingface.co/datasets/rhasspy/piper-checkpoints/tree/main/de/de_DE/thorsten/high][Torsten High]] model as a base and fine tuned it
+   via the voice lines, directly coming from the game files of Portal 1 and Portal 2 to
+   replicate the German GLaDOS voice for piper.
+
+   Training has been performed on an RTX 4000 with 8 GB of VRAM for over more than 3000 epochs.
+** Dataset & Training
+   I also added /hints on how to build the training dataset/ and the used toolchain for preparing and training the model in this repo.
+   Reasons being:
+   - The the training data is intellectual property and copyright by Valve (I cannot include it here for obvious reasons)
+   - Training a model for piper (as of early 2025) relies on old/outdated tools from 2021 and getting everything up
+     and running can be super frustrating
+
+   *Requirements*
+   - A PC with an nVidia GPU and the proprietary nVidia drivers, CUDA, Docker + Docker as well as the nvidia-container-toolkit installed
+   - Ideally use a linux system (WSL untested but potentially might work)
+   - Basic linux and python knowledge
+*** Build the training dataset
+**** Extract the files from the game
+     The training dataset has been extracted from the Portal 1 and Portal 2 game files.
+     For legal reason, they are not included in this repo. But you can easily extract them from the
+     gamefiles via [[https://developer.valvesoftware.com/wiki/VPKEdit][VPKEdit]]
+     *Portal 1*:
+     - Switch the game to the desired language (Here: German) via Steam
+     - Navigate to =<steam>/steamapps/common/Portal/portal= and open =portal_pak_dir.vpk= with VPKEdit
+     - Inside =portal_pak_dir.vpk=, navigate to =sound/vo/aperture_ai= and extract all =*.wav= files into the folder =raw= inside this git repo
+     *Portal 2:*
+     - Switch the game to the desired language (Here: German) via Steam
+     - Navigate to =<steam>/steamapps/common/portal 2=. Select the subfolder matching the language (here =portal2_german=) and open =pak01_dir.vpk= via VPKEdit
+     - Inside =pak01_dir.vpk=, navigate to =sound/vo/glados= and extract all =*.wav= files (but no subfolders) to the folder =raw= inside this git repo
+     *Portal 2 DLC 1*:
+     - Repeat the steps 1 for *Portal 2* above but now select the =portal2_dlc1_<your language>= folder (if it exists). Here, =portal2_dlc1_german= does exist. Open =pak01_dir.vpk= with VPKEdit
+     - Repeat step 3 of *Portal 2* above but copy the files to =raw= in this git repo
+**** Transcode the files
+     We need to transcode the files. The portal 1 files have a samplerate of 44.1 kHz WAV while the portal2 files are MP3.
+     For training, we need WAV, 16bit (LE), mono PCM with the sampleratres shown below, depending on the model quality we want to train.
+     - x-low, low: 16000 kHz
+     - medium, high: 22050 Hz
+     NOTE: In principle, we can also train on 44100 Hz, however the piper-train then needs to be modified for *training* and *inference* as it only supports
+     Run the following command (needs =ffmpeg= to be installed)
+     #+begin_src sh
+     # Before running the script, first edit the bitrate, that you want
+     ./0_transcode.sh
+     #+end_src
+**** Sort by good/bad samples
+     _Now the annoying part_: Listen to all voice samples, one by one and sort them by good (same voice style, no degradation in quality, no additional none-voice parts or mumble etc) and bad (the opposite)
+     I have written a helper script for this purpose: *1_sort_good_bad.py* (Read the comments in it).
+     _But hold your horses_: Before you perform this annoying job, that can take several hours: I expect the quality of the voice lines to be similar across languges. So you can use my
+     script =1_from_good.py= which uses the =good.txt= file to tag voice samples as *good* or *bad*, based on my decisions made during listening to GLaDOS myself.
+     Run the following command
+     #+begin_src sh
+     ./1_from_good.py
+     #+end_src
+**** Transcribe
+     Now we need to transcribe the files. For this, we need =faster-whisper=. The easiest way to install and use it, is to do this via Docker.
+     But before you do that, you should edit the file =2_transcribe.py= and select the language and model you want to use.
+     
+     Run this to build the docker container(s)
+     #+begin_src sh
+     docker compose up --build -d
+     docker exec -it transcribe bash
+     #+end_src
+     
+     You should now be in the =transcribe= docker container. Go to
+     #+begin_src sh
+     cd /training
+     ./2_transcribe.py
+     #+end_src
+     This will yield a new file =metadata.csv=. _Copy this file to =raw_good=, once transcription has finished_
+*** Training
+    For this, you should use the Docker container, which is provided by this repo.
+    But before you do that, you need to configure the new files:
+    - 3_gen_traindata.sh: Edit the samplerate (16000 for x-low and low, 22050 for medium, high models) and the language code (en, de, ru, fr, ...)
+    - 4_train.sh: Edit the QUALITY, BATCHSIZE, PHONEME_MAX parameters, that suit your training hardware.
+      Also select the CHKPOINT to start from: You ideally do not want to train from scratch but rather from an already exisiting checkpoint.
+      Grab [[https://huggingface.co/datasets/rhasspy/piper-checkpoints/tree/main][one from the piper people]], that fits the model (x-low, low, medium, high) and language, that you want to train.
+      Copy it to =checkpoints= within this repo.
+     
+    Now run the following within this repo (if you haven't it already done for transcription)
+    #+begin_src sh
+    docker compose up --build -d
+    docker exec -it training bash
+    #+end_src
+    This will build and enter the training container and also export training metrics via tensorboard at http://127.0.0.1:6006
+
+    From inside the container, you now need to generate your traindata for the training process
+    #+begin_src sh
+    ./3_gen_traindata.sh
+    #+end_src
+
+    And now, you are ready for training. Simply run
+    #+begin_src sh
+    ./4_train.sh
+    #+end_src
+    inside the container.
+
+    In the case, you need to stop training, you just have to change the path to the checkpoint by setting the =CHCKPOINT= variable in =./4_train.sh=.
+*** Infere the final model
+    After training has finished (either it flattened of or you hit the max epoch limit), you need to export the model to the onnx format.
+    First, edit =5_export.sh= and set the name and also the checkpoint (generally the last trained checkpoint by =4_train.sh=, you want to export the model from
+    From still inside the training docker container, run this command
+    #+begin_src sh
+    ./5_export.sh
+    #+end_src
+    This will generate a =<model_name>.onnx= and =<model_name>.onnx.json= file. The later one needs to be adjusted: Open it in a file editor and and navigate to the line where it reads
+    #+begin_src json
+    "dataset": "",
+    #+end_src
+    and place replace "" with this models name (here: "<model_name>")
+    #+begin_src json
+    "dataset": "de_DE-glados-high"
+    #+end_src
+
+    These two files can now be used by piper

checkpoints/epoch=5999-step=1838414.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c6f8da10b32bf33a12cf526ba2bea4351608ef55292e8abf1a1856da5a5a6d5
+size 998167172

compose.yml

@@ -0,0 +1,23 @@
+services:
+  transcribe:
+    image: linuxserver/faster-whisper:latest
+    container_name: transcribe
+    environment:
+      - NVIDIA_VISIBLE_DEVICES=all                          # GPU Support
+      - NVIDIA_DRIVER_CAPABILITIES=compute,video,utility    # GPU Support
+    runtime: nvidia                                         # GPU Support  
+    volumes:
+      - "./:/training:rw"
+  
+  train:
+    build: .
+    container_name: training
+    ports:
+      - 6006:6006
+    environment:
+      - NVIDIA_VISIBLE_DEVICES=all                          # GPU Support
+      - NVIDIA_DRIVER_CAPABILITIES=compute,video,utility    # GPU Support
+    runtime: nvidia                                         # GPU Support
+    volumes:
+      - "./:/training:rw"
+    shm_size: '4gb'

de_DE-glados-high.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3215c8c2f95b13eb159aa336d5583a128ec49a480b90f9a86b08153b8ffad259
+size 114199011

de_DE-glados-high.onnx.json

@@ -0,0 +1,497 @@
+{
+    "dataset": "de_DE-glados-high",
+    "audio": {
+        "sample_rate": 22050,
+        "quality": "traindata"
+    },
+    "espeak": {
+        "voice": "de"
+    },
+    "language": {
+        "code": "de"
+    },
+    "inference": {
+        "noise_scale": 0.667,
+        "length_scale": 1,
+        "noise_w": 0.8
+    },
+    "phoneme_type": "espeak",
+    "phoneme_map": {},
+    "phoneme_id_map": {
+        " ": [
+            3
+        ],
+        "!": [
+            4
+        ],
+        "\"": [
+            150
+        ],
+        "#": [
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+        ],
+        "$": [
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+        ],
+        "'": [
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+        ],
+        "(": [
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+        ],
+        ")": [
+            7
+        ],
+        ",": [
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+        ],
+        "-": [
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+        ],
+        ".": [
+            10
+        ],
+        "0": [
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+        ],
+        "1": [
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+        ],
+        "2": [
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+        ],
+        "3": [
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+        ],
+        "6": [
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+        ],
+        "7": [
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+        ],
+        "8": [
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+        ],
+        "9": [
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+        ],
+        ":": [
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+        ],
+        ";": [
+            12
+        ],
+        "?": [
+            13
+        ],
+        "X": [
+            156
+        ],
+        "^": [
+            1
+        ],
+        "_": [
+            0
+        ],
+        "a": [
+            14
+        ],
+        "b": [
+            15
+        ],
+        "c": [
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+        ],
+        "d": [
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+        "f": [
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+        ],
+        "h": [
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+        ],
+        "i": [
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+        ],
+        "j": [
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+        ],
+        "k": [
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+        ],
+        "l": [
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+        ],
+        "m": [
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+        ],
+        "n": [
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+        "p": [
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+}

good.txt

@@ -0,0 +1,1110 @@
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raw_bad/.gitignore

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