Spaces:
Sleeping
Sleeping
update
Browse files
main.py
CHANGED
@@ -9,6 +9,7 @@ import logging
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from pathlib import Path
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import platform
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import time
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from project_settings import project_path, log_directory
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import log
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@@ -77,6 +78,15 @@ def process(
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main_logger.info("num_active_paths: {}".format(num_active_paths))
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main_logger.info("in_filename: {}".format(in_filename))
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m_list = models.model_map.get(language)
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if m_list is None:
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raise AssertionError("language invalid: {}".format(language))
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@@ -88,11 +98,8 @@ def process(
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if m_dict is None:
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raise AssertionError("repo_id invalid: {}".format(repo_id))
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local_model_dir = pretrained_model_dir / "huggingface" / repo_id
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out_filename = io.BytesIO()
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audio_convert(in_filename, out_filename)
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nn_model_file = local_model_dir / m_dict["nn_model_file"]
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tokens_file = local_model_dir / m_dict["tokens_file"]
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@@ -107,6 +114,7 @@ def process(
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num_active_paths=num_active_paths,
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)
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now = datetime.now()
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date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
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logging.info(f"Started at {date_time}")
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@@ -119,6 +127,7 @@ def process(
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date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
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end = time.time()
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metadata = torchaudio.info(out_filename)
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duration = metadata.num_frames / 16000
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rtf = (end - start) / duration
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from pathlib import Path
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import platform
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import time
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import tempfile
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from project_settings import project_path, log_directory
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import log
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main_logger.info("num_active_paths: {}".format(num_active_paths))
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main_logger.info("in_filename: {}".format(in_filename))
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# audio convert
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in_filename = Path(in_filename)
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out_filename = Path(tempfile.gettempdir()) / "asr" / in_filename.name
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audio_convert(in_filename=in_filename.as_posix(),
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out_filename=out_filename.as_posix(),
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)
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# model settings
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m_list = models.model_map.get(language)
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if m_list is None:
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raise AssertionError("language invalid: {}".format(language))
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if m_dict is None:
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raise AssertionError("repo_id invalid: {}".format(repo_id))
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# load recognizer
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local_model_dir = pretrained_model_dir / "huggingface" / repo_id
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nn_model_file = local_model_dir / m_dict["nn_model_file"]
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tokens_file = local_model_dir / m_dict["tokens_file"]
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num_active_paths=num_active_paths,
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)
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# transcribe
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now = datetime.now()
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date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
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logging.info(f"Started at {date_time}")
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date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
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end = time.time()
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# statistics
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metadata = torchaudio.info(out_filename)
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duration = metadata.num_frames / 16000
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rtf = (end - start) / duration
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