diff --git a/experiment/pipeline_process_wikineural_ner.py b/experiment/pipeline_process_wikineural_ner.py
index 8526f53c512cef4971e2bf4e67f539340bf68b9d..c0ff45f70b81a0084995b2d5cd8bee9a94c72c01 100644
--- a/experiment/pipeline_process_wikineural_ner.py
+++ b/experiment/pipeline_process_wikineural_ner.py
@@ -24,8 +24,7 @@ def run_word_wer_pipeline(dataset_name: str, asr_name: str):
require_update=False
)
],
- experiment_repository=get_repository(dataset_name),
- relation_manager_provider=record_provider
+ experiment_repository=get_repository(dataset_name)
)
experiment_processor.process()
diff --git a/new_experiment/hf_asr/__init__.py b/new_experiment/hf_asr/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/new_experiment/hf_asr/dutch.py b/new_experiment/hf_asr/dutch.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ff11f0f8dcfc88aa4212e8578fe99f3a4244daa
--- /dev/null
+++ b/new_experiment/hf_asr/dutch.py
@@ -0,0 +1,30 @@
+from transformers import (Wav2Vec2ForCTC, Wav2Vec2Processor)
+import torch
+
+from scipy.io import wavfile
+
+from new_experiment.hf_asr.wav2vec2_hf import Wav2Vec2AsrProcessor
+
+if __name__ == '__main__':
+ # print('start dutch')
+ # model_name = "facebook/wav2vec2-large-xlsr-53-dutch"
+ # # device = "cuda"
+ # device = "cpu"
+ # chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"]' # noqa: W605
+ #
+ # model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
+ # print('model ready')
+ # processor = Wav2Vec2Processor.from_pretrained(model_name)
+ # print('processor ready')
+ #
+ # samplerate, data = wavfile.read('/Users/marcinwatroba/Desktop/001dffb13ab67884b30e75d670bd23e4a4c8e5ca.wav')
+ #
+ # features = processor(data, sampling_rate=samplerate, padding=True, return_tensors="pt")
+ # input_values = features.input_values.to(device)
+ # attention_mask = features.attention_mask.to(device)
+ # with torch.no_grad():
+ # logits = model(input_values, attention_mask=attention_mask).logits
+ # pred_ids = torch.argmax(logits, dim=-1)
+ # result = processor.batch_decode(pred_ids)
+ processor = Wav2Vec2AsrProcessor()
+
diff --git a/new_experiment/hf_asr/wav2vec2_hf.py b/new_experiment/hf_asr/wav2vec2_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9e06a0422872a5fc964242e39f127f2ef0fc945
--- /dev/null
+++ b/new_experiment/hf_asr/wav2vec2_hf.py
@@ -0,0 +1,38 @@
+from typing import Dict, Any
+
+import librosa
+import soundfile
+import torch
+from scipy.io import wavfile
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+from sziszapangma.integration.asr_processor import AsrProcessor
+from sziszapangma.model.model_creators import create_new_word
+
+
+class Wav2Vec2AsrProcessor(AsrProcessor):
+ _model: Wav2Vec2ForCTC
+ _wav2vec2_processor: Wav2Vec2Processor
+ _device: str
+
+ def __init__(self, model_name: str = "facebook/wav2vec2-large-xlsr-53-dutch"):
+ self._device = 'cuda' if torch.cuda.is_available() else 'cpu'
+ self._model = Wav2Vec2ForCTC.from_pretrained(model_name).to(self._device)
+ self._wav2vec2_processor = Wav2Vec2Processor.from_pretrained(model_name)
+
+ def call_recognise(self, file_path: str) -> Dict[str, Any]:
+ # samplerate, data = wavfile.read(file_path)
+ # data, samplerate = soundfile.read(file_path)
+ data, samplerate = librosa.load(file_path)
+ features = self._wav2vec2_processor(data, sampling_rate=samplerate, padding=True, return_tensors="pt")
+ input_values = features.input_values.to(self._device)
+ attention_mask = features.attention_mask.to(self._device)
+ with torch.no_grad():
+ logits = self._model(input_values, attention_mask=attention_mask).logits
+ pred_ids = torch.argmax(logits, dim=-1)
+ result = self._wav2vec2_processor.batch_decode(pred_ids)[0]
+ return {
+ "transcription": [create_new_word(it) for it in result.split()],
+ "full_text": result,
+ "words_time_alignment": None
+ }
diff --git a/new_experiment/pipeline/pipeline_process_asr.py b/new_experiment/pipeline/pipeline_process_asr.py
new file mode 100644
index 0000000000000000000000000000000000000000..79fe1cb2021faf4ac79104d92eb1e0144dcfb00e
--- /dev/null
+++ b/new_experiment/pipeline/pipeline_process_asr.py
@@ -0,0 +1,38 @@
+from new_experiment.hf_asr.wav2vec2_hf import Wav2Vec2AsrProcessor
+from new_experiment.new_dependency_provider import get_experiment_repository, get_minio_audio_record_repository
+from new_experiment.utils.loaded_remote_dataset_helper import LoadedRemoteDatasetHelper
+from new_experiment.utils.property_helper import PropertyHelper
+from sziszapangma.integration.asr_processor import AsrProcessor
+from sziszapangma.integration.experiment_manager import ExperimentManager
+from sziszapangma.integration.task.asr_task import AsrTask
+
+
+def get_asr_processor(asr_name: str) -> AsrProcessor:
+ if asr_name == 'facebook_wav2vec2_large_xlsr_53_dutch':
+ return Wav2Vec2AsrProcessor('facebook/wav2vec2-large-xlsr-53-dutch')
+ raise Exception(f'AsrProcessor not found for name: {asr_name}')
+
+
+def run_spacy_dep_tag_wer_pipeline(dataset_name: str, asr_name: str):
+ repository = get_experiment_repository(dataset_name)
+ record_provider = LoadedRemoteDatasetHelper(repository, get_minio_audio_record_repository(), dataset_name)
+ experiment_processor = ExperimentManager(
+ record_id_iterator=record_provider,
+ processing_tasks=[
+ AsrTask(
+ asr_property_name=PropertyHelper.asr_result(asr_name),
+ task_name=f'SpacyDepTagSentenceWerProcessor___{dataset_name}___{asr_name}',
+ require_update=False,
+ asr_processor=get_asr_processor(asr_name),
+ record_path_provider=record_provider
+ )
+ ],
+ experiment_repository=repository,
+ )
+ experiment_processor.process()
+
+
+if __name__ == '__main__':
+ run_spacy_dep_tag_wer_pipeline('nl_minds14', 'facebook_wav2vec2_large_xlsr_53_dutch')
+ run_spacy_dep_tag_wer_pipeline('nl_google_fleurs', 'facebook_wav2vec2_large_xlsr_53_dutch')
+ run_spacy_dep_tag_wer_pipeline('nl_voxpopuli', 'facebook_wav2vec2_large_xlsr_53_dutch')
diff --git a/new_experiment/pipeline/pipeline_process_wikineural_ner_wer.py b/new_experiment/pipeline/pipeline_process_wikineural_ner_wer.py
new file mode 100644
index 0000000000000000000000000000000000000000..761abb4315150b7fdd9e47c4f531250b038c4507
--- /dev/null
+++ b/new_experiment/pipeline/pipeline_process_wikineural_ner_wer.py
@@ -0,0 +1,34 @@
+from experiment.experiment_dependency_provider import get_record_provider, get_repository
+from experiment.sentence_wer_processor.wikineural_multilingual_ner_transformers_wer_processor_base import \
+ WikineuralMultilingualNerTransformersWerProcessorBase
+from new_experiment.new_dependency_provider import get_experiment_repository, get_minio_audio_record_repository
+from new_experiment.utils.loaded_remote_dataset_helper import LoadedRemoteDatasetHelper
+
+from new_experiment.utils.property_helper import PropertyHelper
+from sziszapangma.integration.experiment_manager import ExperimentManager
+
+_WIKINEURAL = 'wikineural'
+
+
+def run_wikineural_ner_pipeline(dataset_name: str, asr_name: str):
+ repository = get_experiment_repository(dataset_name)
+ record_provider = LoadedRemoteDatasetHelper(repository, get_minio_audio_record_repository(), dataset_name)
+ experiment_processor = ExperimentManager(
+ record_id_iterator=record_provider,
+ processing_tasks=[
+ WikineuralMultilingualNerTransformersWerProcessorBase(
+ gold_transcript_property_name=PropertyHelper.get_gold_transcript_raw(),
+ asr_property_name=PropertyHelper.asr_result(asr_name),
+ alignment_property_name=PropertyHelper.ner_alignment(asr_name, _WIKINEURAL),
+ wer_property_name=PropertyHelper.ner_metrics(asr_name, _WIKINEURAL),
+ task_name=f'WikineuralMultilingualNerTransformersWerProcessorBase___{dataset_name}___{asr_name}',
+ require_update=False
+ )
+ ],
+ experiment_repository=repository
+ )
+ experiment_processor.process()
+
+
+if __name__ == '__main__':
+ run_wikineural_ner_pipeline('de_minds14', 'whisper_tiny')
diff --git a/new_experiment/utils/loaded_remote_dataset_helper.py b/new_experiment/utils/loaded_remote_dataset_helper.py
index 2ead6e3ce754e57bfbef2ed6d017fb65df5fc388..74ffdfbedfd910629baef51c6e28f8ddb3260f42 100644
--- a/new_experiment/utils/loaded_remote_dataset_helper.py
+++ b/new_experiment/utils/loaded_remote_dataset_helper.py
@@ -23,8 +23,9 @@ class LoadedRemoteDatasetHelper(DatasetHelper):
return self._experiment_repository.get_all_record_ids_for_property(PropertyHelper.get_gold_transcript_words())
def get_path(self, record_id: str) -> str:
+ print('get_path')
record_path = Path.home() / f'.cache/asr_benchmark/{self._dataset_name}/{record_id}.wav'
if record_path.exists():
return record_path.as_posix()
- self._minio_audio_record_repository.save_file(record_path, self._dataset_name, record_id)
+ self._minio_audio_record_repository.load_file(record_path, self._dataset_name, record_id)
return record_path.as_posix()
diff --git a/new_experiment/utils/minio_audio_record_repository.py b/new_experiment/utils/minio_audio_record_repository.py
index 0c699c8f5c71566988b05312ae798d7eaca30866..366d83d4d78ce3fc61349a5b53961cca2d5a675e 100644
--- a/new_experiment/utils/minio_audio_record_repository.py
+++ b/new_experiment/utils/minio_audio_record_repository.py
@@ -21,6 +21,7 @@ class MinioAudioRecordRepository:
len(open(local_path, 'rb').read()))
def load_file(self, local_path: Path, dataset_name: str, record_id: str):
+ print('load_file')
record_response: HTTPResponse = self._minio.get_object(self._bucket,
self._get_record_path(dataset_name, record_id))
local_path.parent.mkdir(parents=True, exist_ok=True)
diff --git a/new_experiment/worker.py b/new_experiment/worker.py
index bdc90229b37d4a6d1e4df6382dda35a8139a7ff3..70dd0b2b19d5db2c1476a60fca968fe2a2081731 100644
--- a/new_experiment/worker.py
+++ b/new_experiment/worker.py
@@ -1,7 +1,11 @@
import json
import os
+import functools
+import logging
import pika
+import threading
+import time
from new_experiment.pipeline.pipeline_process_spacy_dep_tag_wer import run_spacy_dep_tag_wer_pipeline
from new_experiment.pipeline.pipeline_process_spacy_ner_wer import run_spacy_ner_wer_pipeline
@@ -20,7 +24,8 @@ _RABBIT_URL = get_param('RABBIT_URL',
def main():
parameters = pika.URLParameters(_RABBIT_URL)
- parameters._heartbeat = 65535
+ parameters._heartbeat = 0
+ # parameters._heartbeat = 65535
connection = pika.BlockingConnection(parameters=parameters)
channel = connection.channel()
channel.basic_qos(prefetch_count=1)
@@ -55,5 +60,71 @@ def main():
connection.close()
+def new_main():
+ LOG_FORMAT = ('%(levelname) -10s %(asctime)s %(name) -30s %(funcName) '
+ '-35s %(lineno) -5d: %(message)s')
+ LOGGER = logging.getLogger(__name__)
+
+ logging.basicConfig(level=logging.DEBUG, format=LOG_FORMAT)
+
+ def ack_message(channel, delivery_tag):
+ """Note that `channel` must be the same pika channel instance via which
+ the message being ACKed was retrieved (AMQP protocol constraint).
+ """
+ if channel.is_open:
+ channel.basic_ack(delivery_tag)
+ else:
+ # Channel is already closed, so we can't ACK this message;
+ # log and/or do something that makes sense for your app in this case.
+ pass
+
+ def do_work(connection, channel, delivery_tag, body):
+ thread_id = threading.get_ident()
+ fmt1 = 'Thread id: {} Delivery tag: {} Message body: {}'
+ LOGGER.info(fmt1.format(thread_id, delivery_tag, body))
+ # Sleeping to simulate 10 seconds of work
+ time.sleep(10)
+ cb = functools.partial(ack_message, channel, delivery_tag)
+ connection.add_callback_threadsafe(cb)
+
+ def on_message(channel, method_frame, header_frame, body, args):
+ (connection, threads) = args
+ delivery_tag = method_frame.delivery_tag
+ t = threading.Thread(target=do_work, args=(connection, channel, delivery_tag, body))
+ t.start()
+ threads.append(t)
+
+ credentials = pika.PlainCredentials('guest', 'guest')
+ # Note: sending a short heartbeat to prove that heartbeats are still
+ # sent even though the worker simulates long-running work
+ parameters = pika.ConnectionParameters('localhost', credentials=credentials, heartbeat=5)
+ connection = pika.BlockingConnection(parameters)
+
+ channel = connection.channel()
+ channel.exchange_declare(exchange="test_exchange", exchange_type="direct", passive=False, durable=True,
+ auto_delete=False)
+ channel.queue_declare(queue="standard", auto_delete=True)
+ channel.queue_bind(queue="standard", exchange="test_exchange", routing_key="standard_key")
+ # Note: prefetch is set to 1 here as an example only and to keep the number of threads created
+ # to a reasonable amount. In production you will want to test with different prefetch values
+ # to find which one provides the best performance and usability for your solution
+ channel.basic_qos(prefetch_count=1)
+
+ threads = []
+ on_message_callback = functools.partial(on_message, args=(connection, threads))
+ channel.basic_consume('standard', on_message_callback)
+
+ try:
+ channel.start_consuming()
+ except KeyboardInterrupt:
+ channel.stop_consuming()
+
+ # Wait for all to complete
+ for thread in threads:
+ thread.join()
+
+ connection.close()
+
+
if __name__ == '__main__':
main()
diff --git a/poetry.lock b/poetry.lock
index 570402e851cf0d9435e7ee8296bcfd7389e113b7..e465bba9f32102d8173879e751568b55d06c3fab 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -5357,6 +5357,57 @@ webencodings = ">=0.4"
doc = ["sphinx", "sphinx_rtd_theme"]
test = ["flake8", "isort", "pytest"]
+[[package]]
+name = "tokenizers"
+version = "0.13.2"
+description = "Fast and Customizable Tokenizers"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
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+ {file = "tokenizers-0.13.2-cp39-cp39-win_amd64.whl", hash = "sha256:486d637b413fddada845a10a45c74825d73d3725da42ccd8796ccd7a1c07a024"},
+ {file = "tokenizers-0.13.2.tar.gz", hash = "sha256:f9525375582fd1912ac3caa2f727d36c86ff8c0c6de45ae1aaff90f87f33b907"},
+]
+
+[package.extras]
+dev = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
+docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
+testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
+
[[package]]
name = "tomli"
version = "2.0.1"
@@ -5512,6 +5563,72 @@ files = [
docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"]
+[[package]]
+name = "transformers"
+version = "4.25.1"
+description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
+category = "main"
+optional = false
+python-versions = ">=3.7.0"
+files = [
+ {file = "transformers-4.25.1-py3-none-any.whl", hash = "sha256:60f1be15e17e4a54373c787c713ec149dabcc63464131ac45611618fe7c2016e"},
+ {file = "transformers-4.25.1.tar.gz", hash = "sha256:6dad398b792d45dc04e9ee7e9e06bf758ab19dca2efc119065e661bb0f8f843b"},
+]
+
+[package.dependencies]
+filelock = "*"
+huggingface-hub = ">=0.10.0,<1.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+pyyaml = ">=5.1"
+regex = "!=2019.12.17"
+requests = "*"
+tokenizers = ">=0.11.1,<0.11.3 || >0.11.3,<0.14"
+tqdm = ">=4.27"
+
+[package.extras]
+accelerate = ["accelerate (>=0.10.0)"]
+all = ["Pillow", "accelerate (>=0.10.0)", "codecarbon (==1.2.0)", "flax (>=0.4.1)", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8)", "optuna", "phonemizer", "protobuf (<=3.20.2)", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio"]
+audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+codecarbon = ["codecarbon (==1.2.0)"]
+deepspeed = ["accelerate (>=0.10.0)", "deepspeed (>=0.6.5)"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.10.0)", "beautifulsoup4", "black (==22.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.6.5)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf (<=3.20.2)", "psutil", "pytest", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "timeout-decorator"]
+dev = ["GitPython (<3.1.19)", "Pillow", "accelerate (>=0.10.0)", "beautifulsoup4", "black (==22.3)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flake8 (>=3.8.3)", "flax (>=0.4.1)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8)", "optuna", "parameterized", "phonemizer", "protobuf (<=3.20.2)", "psutil", "pyctcdecode (>=0.4.0)", "pyknp (>=0.6.1)", "pytest", "pytest-timeout", "pytest-xdist", "ray[tune]", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow", "beautifulsoup4", "black (==22.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flake8 (>=3.8.3)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf (<=3.20.2)", "psutil", "pyctcdecode (>=0.4.0)", "pytest", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx", "timeout-decorator", "tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow", "beautifulsoup4", "black (==22.3)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flake8 (>=3.8.3)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf (<=3.20.2)", "psutil", "pyctcdecode (>=0.4.0)", "pyknp (>=0.6.1)", "pytest", "pytest-timeout", "pytest-xdist", "ray[tune]", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+docs = ["Pillow", "accelerate (>=0.10.0)", "codecarbon (==1.2.0)", "flax (>=0.4.1)", "hf-doc-builder", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8)", "optuna", "phonemizer", "protobuf (<=3.20.2)", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio"]
+docs-specific = ["hf-doc-builder"]
+fairscale = ["fairscale (>0.3)"]
+flax = ["flax (>=0.4.1)", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "optax (>=0.0.8)"]
+flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+ftfy = ["ftfy"]
+integrations = ["optuna", "ray[tune]", "sigopt"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "pyknp (>=0.6.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+modelcreation = ["cookiecutter (==1.7.3)"]
+natten = ["natten (>=0.14.4)"]
+onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
+onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
+optuna = ["optuna"]
+quality = ["GitPython (<3.1.19)", "black (==22.3)", "datasets (!=2.5.0)", "flake8 (>=3.8.3)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)"]
+ray = ["ray[tune]"]
+retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf (<=3.20.2)", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (==22.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf (<=3.20.2)", "psutil", "pytest", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx"]
+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.4,<2.11)", "tensorflow-text", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+timm = ["timm"]
+tokenizers = ["tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
+torch = ["torch (>=1.7,!=1.12.0)"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torchhub = ["filelock", "huggingface-hub (>=0.10.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf (<=3.20.2)", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "tqdm (>=4.27)"]
+vision = ["Pillow"]
+
[[package]]
name = "typer"
version = "0.7.0"
@@ -6025,4 +6142,4 @@ testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools"
[metadata]
lock-version = "2.0"
python-versions = ">=3.8,<3.12"
-content-hash = "4ba6bc6633835103fe1fc4818d8bd408552ee78cde9c17b406e9be200dea28ee"
+content-hash = "db5e937e072440888cfbd5f39f302932dddf5b43ad94fdddc6c7c88841f89700"
diff --git a/pyproject.toml b/pyproject.toml
index 26750172549cb1d252679e44e3a543698d6e1c41..92a8240c33bbd05cad61b8a160f3dcf523f7e9e4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -34,6 +34,7 @@ pika = "^1.3.1"
pyopenssl = "^23.0.0"
nltk = "^3.8.1"
jupyterlab = "^3.5.2"
+transformers = "^4.25.1"
[tool.poetry.group.dev.dependencies]
pytest = "^7.2.0"
diff --git a/sziszapangma/integration/task/asr_task.py b/sziszapangma/integration/task/asr_task.py
index 3c8f444426c7565037603c02c5e3f8d8d1201b8b..5700195d33f314ad1283b9b5d2ec87704ff2b196 100644
--- a/sziszapangma/integration/task/asr_task.py
+++ b/sziszapangma/integration/task/asr_task.py
@@ -32,8 +32,7 @@ class AsrTask(ProcessingTask):
def run_single_process(
self,
record_id: str,
- experiment_repository: ExperimentRepository,
- relation_manager: RelationManager,
+ experiment_repository: ExperimentRepository
) -> None:
file_record_path = self._record_path_provider.get_path(record_id)
print('before call_recognise', flush=True)