Ever wanted to create natural sounding speech from text, clone a voice or sound like someone else? SpeechCraft is ideal for creating voiceovers, audiobooks, or just having fun.
- Text2speech synthesis with the 🐶 Bark model of Suno.ai
- Generate text in different languages
- Supports emotions & singing.
- Speaker generation / embedding generation aka voice cloning
- Voice2voice synthesis: given an audio file, generate a new audio file with the voice of a different speaker.
- Convenient deployment ready web API with FastTaskAPI
- Automatic download of models
Also check-out other socaity projects for generative AI:
- High quality voice2voice with retrieval based voice conversion.
- Face swapping with Face2Face
en_speaker_3.mov.mp4
The hermine voice was generated with the voice_clone_test_voice_1.wav file with around 11 seconds of clear speech.
hermine.mp4
The code runs fine on Windows and Linux.
# from PyPi (without web API)
pip install speechcraft
# with web API
pip install speechcraft[full]
# or from GitHub for the newest version.
pip install git+https://github.com/SocAIty/speechcraftFor windows you will also need to install fairseq from a different source.
python pip install fairseq@https://github.com/Sharrnah/fairseq/releases/download/v0.12.4/fairseq-0.12.4-cp310-cp310-win_amd64.whl
To use a GPU don't forget to install pytorch GPU with your correct
cuda version.
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
- Clone the repository.
- (Optional) Create a virtual environment. With
python -m venv venvand activate it withvenv/Scripts/activate. - Install
pip install . - Don't forget to install fairseq and pytorch GPU.
We provide three ways to use the face swapping functionality.
- Direct module import and inference
- By deploying and calling the web service
- As part of the socaity SDK. # coming soon
from speechcraft import text2voice, voice2embedding, voice2voice
# simple text2speech synthesis
text = "I love society [laughs]! [happy] What a day to make voice overs with artificial intelligence."
audio_numpy, sample_rate = text2voice(text, speaker_name="en_speaker_3")
# speaker embedding generation
embedding = voice2embedding(audio_file="voice_sample_15s.wav", voice_name="hermine").save_to_speaker_lib()
# text2speech synthesis with cloned voice or embedding
audio_with_cloned_voice, sample_rate = text2voice(sample_text, voice=embedding) # also works with voice="hermine"
# voice2voice synthesis
cloned_audio = voice2voice(audio_file="my_audio_file.wav", voice_name_or_embedding_path="hermine")Use the following code to convert and save the audio file with the media-toolkit module.
from media_toolkit import AudioFile
audio = AudioFile().from_np_array(audio_numpy, sr=sample_rate)
audio.save("my_new_audio.wav")Note: The first time your are using speechcraft it will download the models. These files are quite big and can take a while to download.
From python:
from speechcraft.server import start_server
start_server(port=8009)With .bat file
- Start the server by running the provided .bat file "start_server.bat"
2. or by using
python bark/server.py --port 8009make sure the python PYTHONPATH is set to the root of this repository. - To test the server, open
http://localhost:8009/docsin your browser.
Then make post requests to the server with your favorite tool or library. Here are some examples to inference with a python client.
Note: The first time you start the server, it will download the models. This can take a while. If this fails, you can download the files manually or with the model_downloader.py script.
NOTE: The Webservice is built with FastTaskAPI. In this regard, for each request it will create a task and return a job id
We highly recommend to use media-toolkit for file transmission. It will make your life much easier.
from media_toolkit import AudioFile
# text2speech
response = httpx.post("http://localhost:8009/text2voice", params={ "text" : "please contribute", "voice": "en_speaker_3"})
# voice cloning
audio = AudioFile().from_file("myfile.wav")
request = httpx.post(
"http://localhost:8009/voice2embedding", params={ "voice_name" : "hermine"},
files={"audio": audio.to_httpx_send_able_tuple()}
)
# voice2voice
response = httpx.post(
"http://localhost:8009/voice2voice",
params={ "voice_name" : "hermine"},
files={"audio": audio.to_httpx_send_able_tuple()}
)This example shows how to do it with plain python requests.
import requests
# text2speech synthesis
response = requests.post("http://localhost:8009/text2voice", params={ "text" : "please contribute", "voice": "en_speaker_3"})
# Speaker embedding creation
with open("myfile.wav", "rb") as f:
audio = f.read()
response = requests.post(
"http://localhost:8009/voice2embedding",
params={ "voice_name" : "my_new_speaker"},
files={"audio_file": audio}
)
# voice2voice
response = requests.post(
"http://localhost:8009/voice2voice",
params={ "voice_name" : "my_new_speaker"},
files={"audio_file": audio}
)The response is a json that includes the job id and meta information. By sending then a request to the job endpoint you can check the status and progress of the job. If the job is finished, you will get the result, including the swapped image.
import requests
from media_toolkit import AudioFile
# check status of job
response = requests.get(f"http://localhost:8009/api/job/{job.json()['job_id']}")
# convert result to image file
audio = AudioFile().from_bytes(response.json()['result']))If you want it more convenient use fastSDK to built your client, or the socaity SDK.
Bark has been tested and works on both CPU and GPU (pytorch 2.0+, CUDA 11.7 and CUDA 12.0).
On enterprise GPUs and PyTorch nightly, Bark can generate audio in roughly real-time. On older GPUs, default colab, or CPU, inference time might be significantly slower. For older GPUs or CPU you might want to consider using smaller models. Details can be found in out tutorial sections here.
The full version of Bark requires around 12GB of VRAM to hold everything on GPU at the same time.
To use a smaller version of the models, which should fit into 8GB VRAM, set the environment flag SUNO_USE_SMALL_MODELS=True.
If you don't have hardware available or if you want to play with bigger versions of our models, you can also sign up for early access to our model playground here.
Bark is fully generative text-to-audio model devolved for research and demo purposes. It follows a GPT style architecture similar to AudioLM and Vall-E and a quantized Audio representation from EnCodec. It is not a conventional TTS model, but instead a fully generative text-to-audio model capable of deviating in unexpected ways from any given script. Different to previous approaches, the input text prompt is converted directly to audio without the intermediate use of phonemes. It can therefore generalize to arbitrary instructions beyond speech such as music lyrics, sound effects or other non-speech sounds.
Below is a list of some known non-speech sounds, but we are finding more every day. Please let us know if you find patterns that work particularly well on Discord!
[laughter][laughs][sighs][music][gasps][clears throat]—or...for hesitations♪for song lyrics- CAPITALIZATION for emphasis of a word
[MAN]and[WOMAN]to bias Bark toward male and female speakers, respectively
| Language | Status |
|---|---|
| English (en) | ✅ |
| German (de) | ✅ |
| Spanish (es) | ✅ |
| French (fr) | ✅ |
| Hindi (hi) | ✅ |
| Italian (it) | ✅ |
| Japanese (ja) | ✅ |
| Korean (ko) | ✅ |
| Polish (pl) | ✅ |
| Portuguese (pt) | ✅ |
| Russian (ru) | ✅ |
| Turkish (tr) | ✅ |
| Chinese, simplified (zh) | ✅ |
To use a different language use the corresponding voice parameter to it like "de_speaker_1". You find preset voices and languages in the assets folder.
SpeechCraft and Bark is licensed under the MIT License.
Make sure these things are NOT in your voice input: (in no particular order)
- Noise (You can use a noise remover before)
- Music (There are also music remover tools) (Unless you want music in the background)
- A cut-off at the end (This will cause it to try and continue on the generation)
What makes for good prompt audio? (in no particular order)
- Around ~7 to ~15 seconds of voice data
- Clearly spoken
- No weird background noises
- Only one speaker
- Audio which ends after a sentence ends
- Regular/common voice (They usually have more success, it's still capable of cloning complex voices, but not as good at it)
This repository is a merge of the orignal bark repository and bark-voice-cloning-HuBert-quantizer by gitmylo The credit goes to the original authors. Like the original authors, I am also not responsible for any misuse of this repository. Use at your own risk, and please act responsibly. Don't copy and publish the voice of a person without their consent.
Any help with maintaining and extending the package is welcome. Feel free to open an issue or a pull request.
