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Build error
Build error
Duplicate from awacke1/ASR-High-Accuracy-Test
Browse filesCo-authored-by: Aaron C Wacker <awacke1@users.noreply.huggingface.co>
- .gitattributes +27 -0
- README.md +14 -0
- app.py +152 -0
- packages.txt +1 -0
- requirements.txt +10 -0
- test.json +12 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title: 🗣️ASR Wav2Vec2 GRadio Multilingual📄
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emoji: 🗣️ASR💻
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colorFrom: purple
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colorTo: pink
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sdk: gradio
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sdk_version: 3.0.17
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app_file: app.py
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pinned: false
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license: mit
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duplicated_from: awacke1/ASR-High-Accuracy-Test
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import gradio as gr
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import logging
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import sys
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import tempfile
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import numpy as np
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import datetime
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from transformers import pipeline, AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM
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from typing import Optional
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from TTS.utils.manage import ModelManager
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from TTS.utils.synthesizer import Synthesizer
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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handlers=[logging.StreamHandler(sys.stdout)],
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)
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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LARGE_MODEL_BY_LANGUAGE = {
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"Arabic": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-arabic", "has_lm": False},
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"Chinese": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn", "has_lm": False},
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#"Dutch": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-dutch", "has_lm": False},
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"English": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-english", "has_lm": True},
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"Finnish": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-finnish", "has_lm": False},
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"French": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-french", "has_lm": True},
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"German": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-german", "has_lm": True},
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"Greek": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-greek", "has_lm": False},
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"Hungarian": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-hungarian", "has_lm": False},
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"Italian": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-italian", "has_lm": True},
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"Japanese": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "has_lm": False},
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"Persian": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-persian", "has_lm": False},
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"Polish": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-polish", "has_lm": True},
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"Portuguese": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-portuguese", "has_lm": True},
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"Russian": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-russian", "has_lm": True},
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"Spanish": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-spanish", "has_lm": True},
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}
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XLARGE_MODEL_BY_LANGUAGE = {
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"English": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-english", "has_lm": True},
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"Spanish": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-spanish", "has_lm": True},
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"German": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-german", "has_lm": True},
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"Russian": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-russian", "has_lm": True},
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"French": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-french", "has_lm": True},
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"Italian": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-italian", "has_lm": True},
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#"Dutch": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-dutch", "has_lm": False},
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"Polish": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-polish", "has_lm": True},
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"Portuguese": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-portuguese", "has_lm": True},
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}
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# LANGUAGES = sorted(LARGE_MODEL_BY_LANGUAGE.keys())
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# the container given by HF has 16GB of RAM, so we need to limit the number of models to load
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LANGUAGES = sorted(XLARGE_MODEL_BY_LANGUAGE.keys())
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CACHED_MODELS_BY_ID = {}
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def run(input_file, language, decoding_type, history, model_size="300M"):
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logger.info(f"Running ASR {language}-{model_size}-{decoding_type} for {input_file}")
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history = history or []
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if model_size == "300M":
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model = LARGE_MODEL_BY_LANGUAGE.get(language, None)
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else:
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model = XLARGE_MODEL_BY_LANGUAGE.get(language, None)
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if model is None:
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history.append({
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"error_message": f"Model size {model_size} not found for {language} language :("
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})
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elif decoding_type == "LM" and not model["has_lm"]:
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history.append({
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"error_message": f"LM not available for {language} language :("
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})
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else:
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# model_instance = AutoModelForCTC.from_pretrained(model["model_id"])
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model_instance = CACHED_MODELS_BY_ID.get(model["model_id"], None)
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if model_instance is None:
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model_instance = AutoModelForCTC.from_pretrained(model["model_id"])
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CACHED_MODELS_BY_ID[model["model_id"]] = model_instance
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if decoding_type == "LM":
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processor = Wav2Vec2ProcessorWithLM.from_pretrained(model["model_id"])
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asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer,
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feature_extractor=processor.feature_extractor, decoder=processor.decoder)
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else:
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processor = Wav2Vec2Processor.from_pretrained(model["model_id"])
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asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer,
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feature_extractor=processor.feature_extractor, decoder=None)
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transcription = asr(input_file, chunk_length_s=5, stride_length_s=1)["text"]
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logger.info(f"Transcription for {input_file}: {transcription}")
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history.append({
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"model_id": model["model_id"],
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"language": language,
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"model_size": model_size,
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"decoding_type": decoding_type,
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"transcription": transcription,
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"error_message": None
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})
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html_output = "<div class='result'>"
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for item in history:
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if item["error_message"] is not None:
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html_output += f"<div class='result_item result_item_error'>{item['error_message']}</div>"
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else:
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url_suffix = " + LM" if item["decoding_type"] == "LM" else ""
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html_output += "<div class='result_item result_item_success'>"
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html_output += f'<strong><a target="_blank" href="https://huggingface.co/{item["model_id"]}">{item["model_id"]}{url_suffix}</a></strong><br/><br/>'
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html_output += f'{item["transcription"]}<br/>'
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html_output += "</div>"
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html_output += "</div>"
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return html_output, history
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gr.Interface(
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run,
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inputs=[
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#gr.inputs.Audio(source="microphone", type="filepath", label="Record something..."),
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gr.Audio(source="microphone", type='filepath', streaming=True),
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#gr.inputs.Audio(source="microphone", type="filepath", label="Record something...", streaming="True"),
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gr.inputs.Radio(label="Language", choices=LANGUAGES),
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gr.inputs.Radio(label="Decoding type", choices=["greedy", "LM"]),
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# gr.inputs.Radio(label="Model size", choices=["300M", "1B"]),
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"state"
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],
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outputs=[
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gr.outputs.HTML(label="Outputs"),
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"state"
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],
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title="🗣️NLP ASR Wav2Vec2 GR📄",
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description="",
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css="""
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.result {display:flex;flex-direction:column}
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.result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%}
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.result_item_success {background-color:mediumaquamarine;color:white;align-self:start}
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.result_item_error {background-color:#ff7070;color:white;align-self:start}
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""",
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allow_screenshot=False,
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allow_flagging="never",
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theme="grass",
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live=True # test1
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).launch(enable_queue=True)
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packages.txt
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ffmpeg
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requirements.txt
ADDED
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transformers
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torch
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pyctcdecode
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pypi-kenlm
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streamlit
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google-cloud-firestore
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firebase-admin
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Werkzeug==2.0.3
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huggingface_hub==0.4.0
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TTS==0.2.1
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test.json
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{
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"type": "service_account",
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"project_id": "clinical-nlp-b9117",
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"private_key_id": "6972d02311e8ee0c5b582551fbcf9c99b9169b58",
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5 |
+
"private_key": "-----BEGIN PRIVATE KEY-----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\nh8qPMwuMcQ/vKGhl3xLYo9M=\n-----END PRIVATE KEY-----\n",
|
6 |
+
"client_email": "firebase-adminsdk-qaxaj@clinical-nlp-b9117.iam.gserviceaccount.com",
|
7 |
+
"client_id": "117623958723912081118",
|
8 |
+
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
|
9 |
+
"token_uri": "https://oauth2.googleapis.com/token",
|
10 |
+
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
|
11 |
+
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-qaxaj%40clinical-nlp-b9117.iam.gserviceaccount.com"
|
12 |
+
}
|