 I recently interviewed a person in Japanese. I need to transcribe the interview in order to find relevant quotes for my article.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Fill-Mask\', \'framework\': \'Transformers\', \'functionality\': \'Fill-Mask\', \'api_name\': \'cl-tohoku/bert-base-japanese\', \'api_call\': "AutoModelForMaskedLM.from_pretrained(\'cl-tohoku/bert-base-japanese\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "fill_mask(\'[MASK]\')", \'performance\': {\'dataset\': \'wikipedia\', \'accuracy\': \'N/A\'}, \'description\': \'This is a BERT model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization.\'}', metadata={})]


 Our startup produces a podcast editing software. We need to convert speech to text automatically for transcription purposes.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'SYSPIN/Telugu_Male_TTS\', \'api_call\': "pipeline(\'text-to-speech\', model=\'SYSPIN/Telugu_Male_TTS\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Telugu Male Text-to-Speech model using the ESPnet framework, provided by Hugging Face.\'}', metadata={})]


 Lately, the quality of our audio has degraded due to background noise, and our robot needs to process speech to understand voice commands. Please help.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'CTranslate2\', \'functionality\': \'Automatic Speech Recognition\', \'api_name\': \'guillaumekln/faster-whisper-large-v2\', \'api_call\': "WhisperModel(\'large-v2\')", \'api_arguments\': [\'audio.mp3\'], \'python_environment_requirements\': [\'faster_whisper\'], \'example_code\': \'from faster_whisper import WhisperModel\\nmodel = WhisperModel(large-v2)\\nsegments, info = model.transcribe(audio.mp3)\\nfor segment in segments:\\n print([%.2fs -> %.2fs] %s % (segment.start, segment.end, segment.text))\', \'performance\': {\'dataset\': \'99 languages\', \'accuracy\': \'Not provided\'}, \'description\': \'Whisper large-v2 model for CTranslate2. This model can be used in CTranslate2 or projets based on CTranslate2 such as faster-whisper.\'}', metadata={})]


 Create a recommendation engine for a podcast platform that enhances the audio quality of low-quality recordings before recommending it to users seeking high-quality content.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Voice Activity Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Voice Activity Detection, Speech-to-Noise Ratio, and C50 Room Acoustics Estimation\', \'api_name\': \'pyannote/brouhaha\', \'api_call\': "Model.from_pretrained(\'pyannote/brouhaha\', use_auth_token=\'ACCESS_TOKEN_GOES_HERE\')", \'api_arguments\': [\'audio.wav\'], \'python_environment_requirements\': [\'pyannote-audio\', \'brouhaha-vad\'], \'example_code\': [\'from pyannote.audio import Model\', \'model = Model.from_pretrained(pyannote/brouhaha, use_auth_token=ACCESS_TOKEN_GOES_HERE)\', \'from pyannote.audio import Inference\', \'inference = Inference(model)\', \'output = inference(audio.wav)\', \'for frame, (vad, snr, c50) in output:\', \'  t = frame.middle\', \'  print(f{t:8.3f} vad={100*vad:.0f}% snr={snr:.0f} c50={c50:.0f})\'], \'performance\': {\'dataset\': \'LibriSpeech, AudioSet, EchoThief, MIT-Acoustical-Reverberation-Scene\', \'accuracy\': \'Not provided\'}, \'description\': \'Brouhaha is a joint voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation model. It is based on the PyTorch framework and uses the pyannote.audio library.\'}', metadata={})]


 Create a system that translates and synthesizes speech from one language to another using the given model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'Fairseq\', \'functionality\': \'Speech-to-speech translation\', \'api_name\': \'facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur\', \'api_call\': "load_model_ensemble_and_task_from_hf_hub(\'facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur\')", \'api_arguments\': {\'audio\': \'16000Hz mono channel audio\'}, \'python_environment_requirements\': [\'fairseq\', \'hub_utils\', \'huggingface_hub\', \'IPython.display\', \'torchaudio\'], \'example_code\': [\'import json\', \'import os\', \'from pathlib import Path\', \'import IPython.display as ipd\', \'from fairseq import hub_utils\', \'from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\', \'from fairseq.models.speech_to_text.hub_interface import S2THubInterface\', \'from fairseq.models.text_to_speech import CodeHiFiGANVocoder\', \'from fairseq.models.text_to_speech.hub_interface import VocoderHubInterface\', \'from huggingface_hub import snapshot_download\', \'import torchaudio\', \'cache_dir = os.getenv(HUGGINGFACE_HUB_CACHE)\', \'models, cfg, task = load_model_ensemble_and_task_from_hf_hub(\', \'facebook/xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022,\', \'arg_overrides={config_yaml: config.yaml, task: speech_to_text},\', \'cache_dir=cache_dir,\', \')\', \'model = models[0].cpu()\', \'cfg[task].cpu = True\', \'generator = task.build_generator([model], cfg)\', \'# requires 16000Hz mono channel audio\', \'audio, _ = torchaudio.load(/Users/lpw/git/api-inference-community/docker_images/fairseq/tests/samples/sample2.flac)\', \'sample = S2THubInterface.get_model_input(task, audio)\', \'unit = S2THubInterface.get_prediction(task, model, generator, sample)\', \'library_name = fairseq\', \'cache_dir = (\', \' cache_dir or (Path.home() / .cache / library_name).as_posix()\', \')\', \'cache_dir = snapshot_download(\', \' ffacebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur, cache_dir=cache_dir, library_name=library_name\', \')\', \'x = hub_utils.from_pretrained(\', \' cache_dir,\', \' model.pt,\', \' .,\', \' archive_map=CodeHiFiGANVocoder.hub_models(),\', \' config_yaml=config.json,\', \' fp16=False,\', \' is_vocoder=True,\', \')\', "with open(f{x[\'args\'][\'data\']}/config.json) as f:", \' vocoder_cfg = json.load(f)\', \'assert (\', \' len(x[args][model_path]) == 1\', \'), Too many vocoder models in the input\', \'vocoder = CodeHiFiGANVocoder(x[args][model_path][0], vocoder_cfg)\', \'tts_model = VocoderHubInterface(vocoder_cfg, vocoder)\', \'tts_sample = tts_model.get_model_input(unit)\', \'wav, sr = tts_model.get_prediction(tts_sample)\', \'ipd.Audio(wav, rate=sr)\'], \'performance\': {\'dataset\': \'covost2\', \'accuracy\': None}, \'description\': \'Speech-to-speech translation model from fairseq S2UT (paper/code) for Spanish-English. Trained on mTEDx, CoVoST 2, Europarl-ST, and VoxPopuli.\'}', metadata={})]


 I have a single channel audio recording containing the voices of two speakers very close, and I would like to separate the voices in this audio recording.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Voice Activity Detection\', \'framework\': \'pyannote.audio\', \'functionality\': \'Speaker diarization\', \'api_name\': \'johnislarry/cloned-pyannote-speaker-diarization-endpoint\', \'api_call\': "Pipeline.from_pretrained(\'pyannote/speaker-diarization@2.1\',use_auth_token=\'ACCESS_TOKEN_GOES_HERE\')", \'api_arguments\': [\'num_speakers\', \'min_speakers\', \'max_speakers\', \'segmentation_onset\'], \'python_environment_requirements\': \'pyannote.audio 2.0\', \'example_code\': {\'load_pipeline\': \'from pyannote.audio import Pipeline\\npipeline = Pipeline.from_pretrained(pyannote/speaker-diarization@2022.07)\', \'apply_pipeline\': \'diarization = pipeline(audio.wav)\', \'save_output\': \'with open(audio.rttm, w) as rttm:\\n  diarization.write_rttm(rttm)\'}, \'performance\': {\'dataset\': [{\'name\': \'AISHELL-4\', \'accuracy\': {\'DER%\': 14.61, \'FA%\': 3.31, \'Miss%\': 4.35, \'Conf%\': 6.95}}, {\'name\': \'AMI Mix-Headset only_words\', \'accuracy\': {\'DER%\': 18.21, \'FA%\': 3.28, \'Miss%\': 11.07, \'Conf%\': 3.87}}, {\'name\': \'AMI Array1-01 only_words\', \'accuracy\': {\'DER%\': 29.0, \'FA%\': 2.71, \'Miss%\': 21.61, \'Conf%\': 4.68}}, {\'name\': \'CALLHOME Part2\', \'accuracy\': {\'DER%\': 30.24, \'FA%\': 3.71, \'Miss%\': 16.86, \'Conf%\': 9.66}}, {\'name\': \'DIHARD 3 Full\', \'accuracy\': {\'DER%\': 20.99, \'FA%\': 4.25, \'Miss%\': 10.74, \'Conf%\': 6.0}}, {\'name\': \'REPERE Phase 2\', \'accuracy\': {\'DER%\': 12.62, \'FA%\': 1.55, \'Miss%\': 3.3, \'Conf%\': 7.76}}, {\'name\': \'VoxConverse v0.0.2\', \'accuracy\': {\'DER%\': 12.76, \'FA%\': 3.45, \'Miss%\': 3.85, \'Conf%\': 5.46}}]}, \'description\': \'This API provides speaker diarization functionality using the pyannote.audio framework. It is capable of processing audio files and outputting speaker diarization results in RTTM format. The API supports providing the number of speakers, minimum and maximum number of speakers, and adjusting the segmentation onset threshold.\'}', metadata={})]


 We are building an app to improve people's public speaking skills. We want to analyze their emotional speech as feedback.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'emotion\', \'api_name\': \'bhadresh-savani/distilbert-base-uncased-emotion\', \'api_call\': "pipeline(\'text-classification\', model=\'bhadresh-savani/distilbert-base-uncased-emotion\', return_all_scores=True)", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "prediction = classifier(\'I love using transformers. The best part is wide range of support and its easy to use\')", \'performance\': {\'dataset\': \'Twitter-Sentiment-Analysis\', \'accuracy\': 0.938}, \'description\': "Distilbert is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It\'s smaller, faster than Bert and any other Bert-based model. Distilbert-base-uncased finetuned on the emotion dataset using HuggingFace Trainer."}', metadata={})]


 A psychology company is building a revolutionary means to detect emotions of its clients. Help them create a system to understand emotions from spoken words.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Emotion Classification\', \'api_name\': \'j-hartmann/emotion-english-distilroberta-base\', \'api_call\': "pipeline(\'text-classification\', model=\'j-hartmann/emotion-english-distilroberta-base\', return_all_scores=True)", \'api_arguments\': {\'text\': \'string\'}, \'python_environment_requirements\': {\'transformers\': \'latest\'}, \'example_code\': \'from transformers import pipeline\\nclassifier = pipeline(text-classification, model=j-hartmann/emotion-english-distilroberta-base, return_all_scores=True)\\nclassifier(I love this!)\', \'performance\': {\'dataset\': \'Balanced subset from 6 diverse datasets\', \'accuracy\': \'66%\'}, \'description\': "This model classifies emotions in English text data. It predicts Ekman\'s 6 basic emotions, plus a neutral class: anger, disgust, fear, joy, neutral, sadness, and surprise. The model is a fine-tuned checkpoint of DistilRoBERTa-base."}', metadata={})]


 We are a company manufacturing AI-based toothbrushes for children. We want to analyze the emotion of children while they brush their teeth.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'emotion\', \'api_name\': \'bhadresh-savani/distilbert-base-uncased-emotion\', \'api_call\': "pipeline(\'text-classification\', model=\'bhadresh-savani/distilbert-base-uncased-emotion\', return_all_scores=True)", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "prediction = classifier(\'I love using transformers. The best part is wide range of support and its easy to use\')", \'performance\': {\'dataset\': \'Twitter-Sentiment-Analysis\', \'accuracy\': 0.938}, \'description\': "Distilbert is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It\'s smaller, faster than Bert and any other Bert-based model. Distilbert-base-uncased finetuned on the emotion dataset using HuggingFace Trainer."}', metadata={})]


 I am a hearing impaired individual who relies on technology to lead a qualitative life. Develop an application that measures noise levels in the environment to help me decide if it's suitable for communication.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Voice Activity Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Voice Activity Detection, Speech-to-Noise Ratio, and C50 Room Acoustics Estimation\', \'api_name\': \'pyannote/brouhaha\', \'api_call\': "Model.from_pretrained(\'pyannote/brouhaha\', use_auth_token=\'ACCESS_TOKEN_GOES_HERE\')", \'api_arguments\': [\'audio.wav\'], \'python_environment_requirements\': [\'pyannote-audio\', \'brouhaha-vad\'], \'example_code\': [\'from pyannote.audio import Model\', \'model = Model.from_pretrained(pyannote/brouhaha, use_auth_token=ACCESS_TOKEN_GOES_HERE)\', \'from pyannote.audio import Inference\', \'inference = Inference(model)\', \'output = inference(audio.wav)\', \'for frame, (vad, snr, c50) in output:\', \'  t = frame.middle\', \'  print(f{t:8.3f} vad={100*vad:.0f}% snr={snr:.0f} c50={c50:.0f})\'], \'performance\': {\'dataset\': \'LibriSpeech, AudioSet, EchoThief, MIT-Acoustical-Reverberation-Scene\', \'accuracy\': \'Not provided\'}, \'description\': \'Brouhaha is a joint voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation model. It is based on the PyTorch framework and uses the pyannote.audio library.\'}', metadata={})]


 Can you classify the audio clip to determine whether it is silent or contains speech?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'CTranslate2\', \'functionality\': \'Automatic Speech Recognition\', \'api_name\': \'guillaumekln/faster-whisper-large-v2\', \'api_call\': "WhisperModel(\'large-v2\')", \'api_arguments\': [\'audio.mp3\'], \'python_environment_requirements\': [\'faster_whisper\'], \'example_code\': \'from faster_whisper import WhisperModel\\nmodel = WhisperModel(large-v2)\\nsegments, info = model.transcribe(audio.mp3)\\nfor segment in segments:\\n print([%.2fs -> %.2fs] %s % (segment.start, segment.end, segment.text))\', \'performance\': {\'dataset\': \'99 languages\', \'accuracy\': \'Not provided\'}, \'description\': \'Whisper large-v2 model for CTranslate2. This model can be used in CTranslate2 or projets based on CTranslate2 such as faster-whisper.\'}', metadata={})]


 I need to predict digit categories based on some tabular data inputs. I need a pretrained model to use for classification.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Hugging Face\', \'functionality\': \'Carbon Emissions Prediction\', \'api_name\': \'bibekbehera/autotrain-numeric_prediction-40376105019\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'bibekbehera/autotrain-data-numeric_prediction\', \'accuracy\': {\'Loss\': 0.152, \'R2\': 0.659, \'MSE\': 0.023, \'MAE\': 0.062, \'RMSLE\': 0.105}}, \'description\': \'A tabular regression model trained with AutoTrain to predict carbon emissions based on input features.\'}', metadata={})]


 In surveillance operations, transcripts are used to turn the audio feed into chunks such that after processing transcripts there are no speakers talking over each other in the output.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'CTranslate2\', \'functionality\': \'Automatic Speech Recognition\', \'api_name\': \'guillaumekln/faster-whisper-large-v2\', \'api_call\': "WhisperModel(\'large-v2\')", \'api_arguments\': [\'audio.mp3\'], \'python_environment_requirements\': [\'faster_whisper\'], \'example_code\': \'from faster_whisper import WhisperModel\\nmodel = WhisperModel(large-v2)\\nsegments, info = model.transcribe(audio.mp3)\\nfor segment in segments:\\n print([%.2fs -> %.2fs] %s % (segment.start, segment.end, segment.text))\', \'performance\': {\'dataset\': \'99 languages\', \'accuracy\': \'Not provided\'}, \'description\': \'Whisper large-v2 model for CTranslate2. This model can be used in CTranslate2 or projets based on CTranslate2 such as faster-whisper.\'}', metadata={})]


 I work at GreenTech, a startup that provides eco-friendly solutions, and need to determine if a given set of input data will result in high carbon emissions or not.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'tejas23/autotrain-amx2-1702259728\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'Validation Metrics\', \'accuracy\': 0.831}, \'description\': \'A multi-class classification model trained using AutoTrain to predict CO2 emissions based on tabular data.\'}', metadata={})]


 Can you figure a way to predict electricity consumption? We need to better allocate resources.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'pachi107/autotrain-in-class-test-1780161764\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data.csv\'], \'python_environment_requirements\': [\'json\', \'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(data.csv)\\ndata = data[features]\\ndata.columns = [feat_ + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'Validation Metrics\', \'accuracy\': 0.974}, \'description\': \'A binary classification model for predicting CO2 emissions based on tabular data. Trained using AutoTrain with a model ID of 1780161764.\'}', metadata={})]