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replicate demo

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chenxwh
2024-04-05 17:23:39 +00:00
parent 49a648fa54
commit 023d4b1c6c
2 changed files with 119 additions and 38 deletions
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156
predict.py
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@ -11,7 +11,9 @@ import torchaudio
import shutil
import subprocess
import sys
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
os.environ["USER"] = getpass.getuser()
from data.tokenizer import (
@ -21,9 +23,14 @@ from data.tokenizer import (
from cog import BasePredictor, Input, Path
from models import voicecraft
from inference_tts_scale import inference_one_sample
from edit_utils import get_span
from inference_speech_editing_scale import get_mask_interval
from inference_speech_editing_scale import (
inference_one_sample as inference_one_sample_editing,
)
ENV_NAME = "myenv"
# sys.path.append(f"/cog/miniconda/envs/{ENV_NAME}/lib/python3.10/site-packages")
MODEL_URL = "https://weights.replicate.delivery/default/VoiceCraft.tar"
MODEL_CACHE = "model_cache"
@ -63,22 +70,33 @@ class Predictor(BasePredictor):
def predict(
self,
task: str = Input(
description="Choose a task. For zero-shot text-to-speech, you also need to specify the cut_off_sec of the original audio to be used for zero-shot generation and the transcript until the cut_off_sec",
choices=[
"speech_editing-substitution",
"speech_editing-insertion",
"speech_editing-sdeletion",
"zero-shot text-to-speech",
],
default="speech_editing-substitution",
),
orig_audio: Path = Input(description="Original audio file"),
orig_transcript: str = Input(
description="Transcript of the original audio file. You can use models such as https://replicate.com/openai/whisper and https://replicate.com/vaibhavs10/incredibly-fast-whisper to get the transcript (and modify it if it's not accurate)",
),
cut_off_sec: float = Input(
description="The first seconds of the original audio that are used for zero-shot text-to-speech (TTS). 3 sec of reference is generally enough for high quality voice cloning, but longer is generally better, try e.g. 3~6 sec",
default=3.01,
),
orig_transcript_until_cutoff_time: str = Input(
description="Transcript of the original audio file until the cut_off_sec specified above. This process will be improved and made automatically later",
),
target_transcript: str = Input(
description="Transcript of the target audio file",
),
cut_off_sec: float = Input(
description="Valid/Required for zero-shot text-to-speech task. The first seconds of the original audio that are used for zero-shot text-to-speech (TTS). 3 sec of reference is generally enough for high quality voice cloning, but longer is generally better, try e.g. 3~6 sec",
default=3.01,
),
orig_transcript_until_cutoff_time: str = Input(
description="Valid/Required for zero-shot text-to-speech task. Transcript of the original audio file until the cut_off_sec specified above. This process will be improved and made automatically later",
default=None,
),
temperature: float = Input(
description="Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic,
description="Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic",
ge=0.01,
le=5,
default=1,
@ -89,6 +107,10 @@ class Predictor(BasePredictor):
le=1.0,
default=0.8,
),
stop_repetition: int = Input(
default=-1,
description=" -1 means do not adjust prob of silence tokens. if there are long silence or unnaturally strecthed words, increase sample_batch_size to 2, 3 or even 4",
),
sampling_rate: int = Input(
description="Specify the sampling rate of the audio codec", default=16000
),
@ -97,20 +119,27 @@ class Predictor(BasePredictor):
),
) -> Path:
"""Run a single prediction on the model"""
if task == "zero-shot text-to-speech":
assert (
orig_transcript_until_cutoff_time is not None
), "Please provide orig_transcript_until_cutoff_time for zero-shot text-to-speech task."
if seed is None:
seed = int.from_bytes(os.urandom(2), "big")
print(f"Using seed: {seed}")
seed_everything(seed)
temp_folder = "exp_temp"
temp_folder = "exp_dir"
if os.path.exists(temp_folder):
shutil.rmtree(temp_folder)
os.makedirs(temp_folder)
os.system(f"cp {str(orig_audio)} {temp_folder}")
# filename = os.path.splitext(orig_audio.split("/")[-1])[0]
with open(f"{temp_folder}/orig_audio_file.txt", "w") as f:
filename = "orig_audio"
shutil.copy(orig_audio, f"{temp_folder}/{filename}.wav")
with open(f"{temp_folder}/{filename}.txt", "w") as f:
f.write(orig_transcript)
# run MFA to get the alignment
@ -121,26 +150,61 @@ class Predictor(BasePredictor):
subprocess.run(command, shell=True, check=True)
except subprocess.CalledProcessError as e:
print("Error:", e)
raise RuntimeError("Error running Alignment")
print("Alignment done!")
audio_fn = str(orig_audio) # f"{temp_folder}/{filename}.wav"
align_fn = f"{align_temp}/{filename}.csv"
audio_fn = f"{temp_folder}/{filename}.wav"
info = torchaudio.info(audio_fn)
audio_dur = info.num_frames / info.sample_rate
assert (
cut_off_sec < audio_dur
), f"cut_off_sec {cut_off_sec} is larger than the audio duration {audio_dur}"
prompt_end_frame = int(cut_off_sec * info.sample_rate)
# hyperparameters for inference
left_margin = 0.08
right_margin = 0.08
codec_sr = 50
top_k = 0
silence_tokens = [1388, 1898, 131]
kvcache = 1 # NOTE if OOM, change this to 0, or try the 330M model
kvcache = 1 if task == "zero-shot text-to-speech" else 0
# NOTE adjust the below three arguments if the generation is not as good
stop_repetition = 3 # NOTE if the model generate long silence, reduce the stop_repetition to 3, 2 or even 1
sample_batch_size = 4 # NOTE: if the if there are long silence or unnaturally strecthed words, increase sample_batch_size to 5 or higher. What this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest. So if the speech rate of the generated is too fast change it to a smaller number.
if task == "":
assert (
cut_off_sec < audio_dur
), f"cut_off_sec {cut_off_sec} is larger than the audio duration {audio_dur}"
prompt_end_frame = int(cut_off_sec * info.sample_rate)
else:
edit_type = task.split("-")[-1]
orig_span, new_span = get_span(
orig_transcript, target_transcript, edit_type
)
if orig_span[0] > orig_span[1]:
RuntimeError(f"example {audio_fn} failed")
if orig_span[0] == orig_span[1]:
orig_span_save = [orig_span[0]]
else:
orig_span_save = orig_span
if new_span[0] == new_span[1]:
new_span_save = [new_span[0]]
else:
new_span_save = new_span
orig_span_save = ",".join([str(item) for item in orig_span_save])
new_span_save = ",".join([str(item) for item in new_span_save])
start, end = get_mask_interval(align_fn, orig_span_save, edit_type)
# span in codec frames
morphed_span = (
max(start - left_margin, 1 / codec_sr),
min(end + right_margin, audio_dur),
) # in seconds
mask_interval = [
[round(morphed_span[0] * codec_sr), round(morphed_span[1] * codec_sr)]
]
mask_interval = torch.LongTensor(mask_interval) # [M,2], M==1 for now
decode_config = {
"top_k": top_k,
"top_p": top_p,
@ -150,27 +214,45 @@ class Predictor(BasePredictor):
"codec_audio_sr": sampling_rate,
"codec_sr": codec_sr,
"silence_tokens": silence_tokens,
"sample_batch_size": sample_batch_size,
}
concated_audio, gen_audio = inference_one_sample(
self.model,
self.ckpt["config"],
self.phn2num,
self.text_tokenizer,
self.audio_tokenizer,
audio_fn,
orig_transcript_until_cutoff_time.strip() + "" + target_transcript.strip(),
self.device,
decode_config,
prompt_end_frame,
)
if task == "zero-shot text-to-speech":
decode_config["sample_batch_size"] = sample_batch_size
concated_audio, gen_audio = inference_one_sample(
self.model,
self.ckpt["config"],
self.phn2num,
self.text_tokenizer,
self.audio_tokenizer,
audio_fn,
orig_transcript_until_cutoff_time.strip()
+ ""
+ target_transcript.strip(),
self.device,
decode_config,
prompt_end_frame,
)
else:
orig_audio, gen_audio = inference_one_sample_editing(
self.model,
self.ckpt["config"],
self.phn2num,
self.text_tokenizer,
self.audio_tokenizer,
audio_fn,
target_transcript,
mask_interval,
self.device,
decode_config,
)
# save segments for comparison
concated_audio, gen_audio = concated_audio[0].cpu(), gen_audio[0].cpu()
gen_audio = gen_audio[0].cpu()
out = "/tmp/out.wav"
torchaudio.save(out, gen_audio, sampling_rate)
torchaudio.save("out.wav", gen_audio, sampling_rate)
return Path(out)