|
--- |
|
license: cc-by-nc-4.0 |
|
library_name: fairseq |
|
task: text-to-speech |
|
tags: |
|
- fairseq |
|
- audio |
|
- text-to-speech |
|
language: en |
|
datasets: |
|
- mtedx |
|
- covost2 |
|
- europarl_st |
|
- voxpopuli |
|
widget: |
|
- example_title: Common Voice sample 1 |
|
src: https://huggingface.co/facebook/xm_transformer_600m-es_en-multi_domain/resolve/main/common_voice_es_19966634.flac |
|
--- |
|
## unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur |
|
|
|
Speech-to-speech translation model from fairseq S2UT ([paper](https://arxiv.org/abs/2204.02967)/[code](https://github.com/facebookresearch/fairseq/blob/main/examples/speech_to_speech/docs/enhanced_direct_s2st_discrete_units.md)): |
|
- Spanish-English |
|
- Trained on mTEDx, CoVoST 2, Europarl-ST and VoxPopuli |
|
|
|
## Usage |
|
|
|
```python |
|
import json |
|
import os |
|
from pathlib import Path |
|
|
|
import IPython.display as ipd |
|
from fairseq import hub_utils |
|
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub |
|
from fairseq.models.speech_to_text.hub_interface import S2THubInterface |
|
from fairseq.models.text_to_speech import CodeHiFiGANVocoder |
|
from fairseq.models.text_to_speech.hub_interface import VocoderHubInterface |
|
|
|
from huggingface_hub import snapshot_download |
|
import torchaudio |
|
|
|
cache_dir = os.getenv("HUGGINGFACE_HUB_CACHE") |
|
|
|
#models, cfg, task = load_model_ensemble_and_task_from_hf_hub( |
|
# "facebook/xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022", |
|
# arg_overrides={"config_yaml": "config.yaml", "task": "speech_to_text"}, |
|
# cache_dir=cache_dir, |
|
# ) |
|
# model = models[0].cpu() |
|
# cfg["task"].cpu = True |
|
# generator = task.build_generator([model], cfg) |
|
|
|
|
|
# # requires 16000Hz mono channel audio |
|
# audio, _ = torchaudio.load("/Users/lpw/git/api-inference-community/docker_images/fairseq/tests/samples/sample2.flac") |
|
|
|
# sample = S2THubInterface.get_model_input(task, audio) |
|
# unit = S2THubInterface.get_prediction(task, model, generator, sample) |
|
|
|
# speech synthesis |
|
library_name = "fairseq" |
|
cache_dir = ( |
|
cache_dir or (Path.home() / ".cache" / library_name).as_posix() |
|
) |
|
cache_dir = snapshot_download( |
|
f"facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur", cache_dir=cache_dir, library_name=library_name |
|
) |
|
|
|
x = hub_utils.from_pretrained( |
|
cache_dir, |
|
"model.pt", |
|
".", |
|
archive_map=CodeHiFiGANVocoder.hub_models(), |
|
config_yaml="config.json", |
|
fp16=False, |
|
is_vocoder=True, |
|
) |
|
|
|
with open(f"{x['args']['data']}/config.json") as f: |
|
vocoder_cfg = json.load(f) |
|
assert ( |
|
len(x["args"]["model_path"]) == 1 |
|
), "Too many vocoder models in the input" |
|
|
|
vocoder = CodeHiFiGANVocoder(x["args"]["model_path"][0], vocoder_cfg) |
|
tts_model = VocoderHubInterface(vocoder_cfg, vocoder) |
|
|
|
tts_sample = tts_model.get_model_input(unit) |
|
wav, sr = tts_model.get_prediction(tts_sample) |
|
|
|
ipd.Audio(wav, rate=sr) |
|
``` |
