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from whisperx.alignment import (
DEFAULT_ALIGN_MODELS_TORCH as DAMT,
DEFAULT_ALIGN_MODELS_HF as DAMHF,
)
from whisperx.utils import TO_LANGUAGE_CODE
import whisperx
import torch
import gc
import os
import soundfile as sf
from IPython.utils import capture # noqa
from .language_configuration import EXTRA_ALIGN, INVERTED_LANGUAGES
from .logging_setup import logger
from .postprocessor import sanitize_file_name
from .utils import remove_directory_contents, run_command
# ZERO GPU CONFIG
import spaces
import copy
import random
import time
def random_sleep():
if os.environ.get("ZERO_GPU") == "TRUE":
print("Random sleep")
sleep_time = round(random.uniform(7.2, 9.9), 1)
time.sleep(sleep_time)
@spaces.GPU(duration=120)
def load_and_transcribe_audio(asr_model, audio, compute_type, language, asr_options, batch_size, segment_duration_limit):
# Load model
model = whisperx.load_model(
asr_model,
os.environ.get("SONITR_DEVICE") if os.environ.get("ZERO_GPU") != "TRUE" else "cuda",
compute_type=compute_type,
language=language,
asr_options=asr_options,
)
# Transcribe audio
result = model.transcribe(
audio,
batch_size=batch_size,
chunk_size=segment_duration_limit,
print_progress=True,
)
del model
gc.collect()
torch.cuda.empty_cache() # noqa
return result
def load_align_and_align_segments(result, audio, DAMHF):
# Load alignment model
model_a, metadata = whisperx.load_align_model(
language_code=result["language"],
device=os.environ.get("SONITR_DEVICE") if os.environ.get("ZERO_GPU") != "TRUE" else "cuda",
model_name=None
if result["language"] in DAMHF.keys()
else EXTRA_ALIGN[result["language"]],
)
# Align segments
alignment_result = whisperx.align(
result["segments"],
model_a,
metadata,
audio,
os.environ.get("SONITR_DEVICE") if os.environ.get("ZERO_GPU") != "TRUE" else "cuda",
return_char_alignments=True,
print_progress=False,
)
# Clean up
del model_a
gc.collect()
torch.cuda.empty_cache() # noqa
return alignment_result
@spaces.GPU(duration=120)
def diarize_audio(diarize_model, audio_wav, min_speakers, max_speakers):
if os.environ.get("ZERO_GPU") == "TRUE":
diarize_model.model.to(torch.device("cuda"))
diarize_segments = diarize_model(
audio_wav,
min_speakers=min_speakers,
max_speakers=max_speakers
)
return diarize_segments
# ZERO GPU CONFIG
ASR_MODEL_OPTIONS = [
"tiny",
"base",
"small",
"medium",
"large",
"large-v1",
"large-v2",
"large-v3",
"distil-large-v2",
"Systran/faster-distil-whisper-large-v3",
"tiny.en",
"base.en",
"small.en",
"medium.en",
"distil-small.en",
"distil-medium.en",
"OpenAI_API_Whisper",
]
COMPUTE_TYPE_GPU = [
"default",
"auto",
"int8",
"int8_float32",
"int8_float16",
"int8_bfloat16",
"float16",
"bfloat16",
"float32"
]
COMPUTE_TYPE_CPU = [
"default",
"auto",
"int8",
"int8_float32",
"int16",
"float32",
]
WHISPER_MODELS_PATH = './WHISPER_MODELS'
def openai_api_whisper(
input_audio_file,
source_lang=None,
chunk_duration=1800
):
info = sf.info(input_audio_file)
duration = info.duration
output_directory = "./whisper_api_audio_parts"
os.makedirs(output_directory, exist_ok=True)
remove_directory_contents(output_directory)
if duration > chunk_duration:
# Split the audio file into smaller chunks with 30-minute duration
cm = f'ffmpeg -i "{input_audio_file}" -f segment -segment_time {chunk_duration} -c:a libvorbis "{output_directory}/output%03d.ogg"'
run_command(cm)
# Get list of generated chunk files
chunk_files = sorted(
[f"{output_directory}/{f}" for f in os.listdir(output_directory) if f.endswith('.ogg')]
)
else:
one_file = f"{output_directory}/output000.ogg"
cm = f'ffmpeg -i "{input_audio_file}" -c:a libvorbis {one_file}'
run_command(cm)
chunk_files = [one_file]
# Transcript
segments = []
language = source_lang if source_lang else None
for i, chunk in enumerate(chunk_files):
from openai import OpenAI
client = OpenAI()
audio_file = open(chunk, "rb")
transcription = client.audio.transcriptions.create(
model="whisper-1",
file=audio_file,
language=language,
response_format="verbose_json",
timestamp_granularities=["segment"],
)
try:
transcript_dict = transcription.model_dump()
except: # noqa
transcript_dict = transcription.to_dict()
if language is None:
logger.info(f'Language detected: {transcript_dict["language"]}')
language = TO_LANGUAGE_CODE[transcript_dict["language"]]
chunk_time = chunk_duration * (i)
for seg in transcript_dict["segments"]:
if "start" in seg.keys():
segments.append(
{
"text": seg["text"],
"start": seg["start"] + chunk_time,
"end": seg["end"] + chunk_time,
}
)
audio = whisperx.load_audio(input_audio_file)
result = {"segments": segments, "language": language}
return audio, result
def find_whisper_models():
path = WHISPER_MODELS_PATH
folders = []
if os.path.exists(path):
for folder in os.listdir(path):
folder_path = os.path.join(path, folder)
if (
os.path.isdir(folder_path)
and 'model.bin' in os.listdir(folder_path)
):
folders.append(folder)
return folders
def transcribe_speech(
audio_wav,
asr_model,
compute_type,
batch_size,
SOURCE_LANGUAGE,
literalize_numbers=True,
segment_duration_limit=15,
):
"""
Transcribe speech using a whisper model.
Parameters:
- audio_wav (str): Path to the audio file in WAV format.
- asr_model (str): The whisper model to be loaded.
- compute_type (str): Type of compute to be used (e.g., 'int8', 'float16').
- batch_size (int): Batch size for transcription.
- SOURCE_LANGUAGE (str): Source language for transcription.
Returns:
- Tuple containing:
- audio: Loaded audio file.
- result: Transcription result as a dictionary.
"""
if asr_model == "OpenAI_API_Whisper":
if literalize_numbers:
logger.info(
"OpenAI's API Whisper does not support "
"the literalization of numbers."
)
return openai_api_whisper(audio_wav, SOURCE_LANGUAGE)
# https://github.com/openai/whisper/discussions/277
prompt = "以下是普通话的句子。" if SOURCE_LANGUAGE == "zh" else None
SOURCE_LANGUAGE = (
SOURCE_LANGUAGE if SOURCE_LANGUAGE != "zh-TW" else "zh"
)
asr_options = {
"initial_prompt": prompt,
"suppress_numerals": literalize_numbers
}
if asr_model not in ASR_MODEL_OPTIONS:
base_dir = WHISPER_MODELS_PATH
if not os.path.exists(base_dir):
os.makedirs(base_dir)
model_dir = os.path.join(base_dir, sanitize_file_name(asr_model))
if not os.path.exists(model_dir):
from ctranslate2.converters import TransformersConverter
quantization = "float32"
# Download new model
try:
converter = TransformersConverter(
asr_model,
low_cpu_mem_usage=True,
copy_files=[
"tokenizer_config.json", "preprocessor_config.json"
]
)
converter.convert(
model_dir,
quantization=quantization,
force=False
)
except Exception as error:
if "File tokenizer_config.json does not exist" in str(error):
converter._copy_files = [
"tokenizer.json", "preprocessor_config.json"
]
converter.convert(
model_dir,
quantization=quantization,
force=True
)
else:
raise error
asr_model = model_dir
logger.info(f"ASR Model: {str(model_dir)}")
audio = whisperx.load_audio(audio_wav)
result = load_and_transcribe_audio(
asr_model, audio, compute_type, SOURCE_LANGUAGE, asr_options, batch_size, segment_duration_limit
)
if result["language"] == "zh" and not prompt:
result["language"] = "zh-TW"
logger.info("Chinese - Traditional (zh-TW)")
return audio, result
def align_speech(audio, result):
"""
Aligns speech segments based on the provided audio and result metadata.
Parameters:
- audio (array): The audio data in a suitable format for alignment.
- result (dict): Metadata containing information about the segments
and language.
Returns:
- result (dict): Updated metadata after aligning the segments with
the audio. This includes character-level alignments if
'return_char_alignments' is set to True.
Notes:
- This function uses language-specific models to align speech segments.
- It performs language compatibility checks and selects the
appropriate alignment model.
- Cleans up memory by releasing resources after alignment.
"""
DAMHF.update(DAMT) # lang align
if (
not result["language"] in DAMHF.keys()
and not result["language"] in EXTRA_ALIGN.keys()
):
logger.warning(
"Automatic detection: Source language not compatible with align"
)
raise ValueError(
f"Detected language {result['language']} incompatible, "
"you can select the source language to avoid this error."
)
if (
result["language"] in EXTRA_ALIGN.keys()
and EXTRA_ALIGN[result["language"]] == ""
):
lang_name = (
INVERTED_LANGUAGES[result["language"]]
if result["language"] in INVERTED_LANGUAGES.keys()
else result["language"]
)
logger.warning(
"No compatible wav2vec2 model found "
f"for the language '{lang_name}', skipping alignment."
)
return result
random_sleep()
result = load_align_and_align_segments(result, audio, DAMHF)
return result
diarization_models = {
"pyannote_3.1": "pyannote/speaker-diarization-3.1",
"pyannote_2.1": "pyannote/speaker-diarization@2.1",
"disable": "",
}
def reencode_speakers(result):
if result["segments"][0]["speaker"] == "SPEAKER_00":
return result
speaker_mapping = {}
counter = 0
logger.debug("Reencode speakers")
for segment in result["segments"]:
old_speaker = segment["speaker"]
if old_speaker not in speaker_mapping:
speaker_mapping[old_speaker] = f"SPEAKER_{counter:02d}"
counter += 1
segment["speaker"] = speaker_mapping[old_speaker]
return result
def diarize_speech(
audio_wav,
result,
min_speakers,
max_speakers,
YOUR_HF_TOKEN,
model_name="pyannote/speaker-diarization@2.1",
):
"""
Performs speaker diarization on speech segments.
Parameters:
- audio_wav (array): Audio data in WAV format to perform speaker
diarization.
- result (dict): Metadata containing information about speech segments
and alignments.
- min_speakers (int): Minimum number of speakers expected in the audio.
- max_speakers (int): Maximum number of speakers expected in the audio.
- YOUR_HF_TOKEN (str): Your Hugging Face API token for model
authentication.
- model_name (str): Name of the speaker diarization model to be used
(default: "pyannote/speaker-diarization@2.1").
Returns:
- result_diarize (dict): Updated metadata after assigning speaker
labels to segments.
Notes:
- This function utilizes a speaker diarization model to label speaker
segments in the audio.
- It assigns speakers to word-level segments based on diarization results.
- Cleans up memory by releasing resources after diarization.
- If only one speaker is specified, each segment is automatically assigned
as the first speaker, eliminating the need for diarization inference.
"""
if max(min_speakers, max_speakers) > 1 and model_name:
try:
diarize_model = whisperx.DiarizationPipeline(
model_name=model_name,
use_auth_token=YOUR_HF_TOKEN,
device=os.environ.get("SONITR_DEVICE"),
)
except Exception as error:
error_str = str(error)
gc.collect()
torch.cuda.empty_cache() # noqa
if "'NoneType' object has no attribute 'to'" in error_str:
if model_name == diarization_models["pyannote_2.1"]:
raise ValueError(
"Accept the license agreement for using Pyannote 2.1."
" You need to have an account on Hugging Face and "
"accept the license to use the models: "
"https://huggingface.co/pyannote/speaker-diarization "
"and https://huggingface.co/pyannote/segmentation "
"Get your KEY TOKEN here: "
"https://hf.co/settings/tokens "
)
elif model_name == diarization_models["pyannote_3.1"]:
raise ValueError(
"New Licence Pyannote 3.1: You need to have an account"
" on Hugging Face and accept the license to use the "
"models: https://huggingface.co/pyannote/speaker-diarization-3.1 " # noqa
"and https://huggingface.co/pyannote/segmentation-3.0 "
)
else:
raise error
random_sleep()
diarize_segments = diarize_audio(diarize_model, audio_wav, min_speakers, max_speakers)
result_diarize = whisperx.assign_word_speakers(
diarize_segments, result
)
for segment in result_diarize["segments"]:
if "speaker" not in segment:
segment["speaker"] = "SPEAKER_00"
logger.warning(
f"No speaker detected in {segment['start']}. First TTS "
f"will be used for the segment text: {segment['text']} "
)
del diarize_model
gc.collect()
torch.cuda.empty_cache() # noqa
else:
result_diarize = result
result_diarize["segments"] = [
{**item, "speaker": "SPEAKER_00"}
for item in result_diarize["segments"]
]
return reencode_speakers(result_diarize)
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