.vscode/PythonImportHelper-v2-Completion.json

[
    {
        "label": "APIRouter",
        "importPath": "fastapi",
        "description": "fastapi",
        "isExtraImport": true,
        "detail": "fastapi",
        "documentation": {}
    },
    {
        "label": "UploadFile",
        "importPath": "fastapi",
        "description": "fastapi",
        "isExtraImport": true,
        "detail": "fastapi",
        "documentation": {}
    },
    {
        "label": "File",
        "importPath": "fastapi",
        "description": "fastapi",
        "isExtraImport": true,
        "detail": "fastapi",
        "documentation": {}
    },
    {
        "label": "Body",
        "importPath": "fastapi",
        "description": "fastapi",
        "isExtraImport": true,
        "detail": "fastapi",
        "documentation": {}
    },
    {
        "label": "HTTPException",
        "importPath": "fastapi",
        "description": "fastapi",
        "isExtraImport": true,
        "detail": "fastapi",
        "documentation": {}
    },
    {
        "label": "status",
        "importPath": "fastapi",
        "description": "fastapi",
        "isExtraImport": true,
        "detail": "fastapi",
        "documentation": {}
    },
    {
        "label": "APIRouter",
        "importPath": "fastapi",
        "description": "fastapi",
        "isExtraImport": true,
        "detail": "fastapi",
        "documentation": {}
    },
    {
        "label": "APIRouter",
        "importPath": "fastapi",
        "description": "fastapi",
        "isExtraImport": true,
        "detail": "fastapi",
        "documentation": {}
    },
    {
        "label": "FastAPI",
        "importPath": "fastapi",
        "description": "fastapi",
        "isExtraImport": true,
        "detail": "fastapi",
        "documentation": {}
    },
    {
        "label": "Request",
        "importPath": "fastapi",
        "description": "fastapi",
        "isExtraImport": true,
        "detail": "fastapi",
        "documentation": {}
    },
    {
        "label": "JSONResponse",
        "importPath": "fastapi.responses",
        "description": "fastapi.responses",
        "isExtraImport": true,
        "detail": "fastapi.responses",
        "documentation": {}
    },
    {
        "label": "HTMLResponse",
        "importPath": "fastapi.responses",
        "description": "fastapi.responses",
        "isExtraImport": true,
        "detail": "fastapi.responses",
        "documentation": {}
    },
    {
        "label": "Annotated",
        "importPath": "typing",
        "description": "typing",
        "isExtraImport": true,
        "detail": "typing",
        "documentation": {}
    },
    {
        "label": "time",
        "kind": 6,
        "isExtraImport": true,
        "importPath": "time",
        "description": "time",
        "detail": "time",
        "documentation": {}
    },
    {
        "label": "os",
        "kind": 6,
        "isExtraImport": true,
        "importPath": "os",
        "description": "os",
        "detail": "os",
        "documentation": {}
    },
    {
        "label": "get_transcription",
        "importPath": "app.transcriber",
        "description": "app.transcriber",
        "isExtraImport": true,
        "detail": "app.transcriber",
        "documentation": {}
    },
    {
        "label": "match",
        "importPath": "app.matcher",
        "description": "app.matcher",
        "isExtraImport": true,
        "detail": "app.matcher",
        "documentation": {}
    },
    {
        "label": "mfcc_similarty_check",
        "importPath": "app.mfcc",
        "description": "app.mfcc",
        "isExtraImport": true,
        "detail": "app.mfcc",
        "documentation": {}
    },
    {
        "label": "voice_router",
        "importPath": "app.routers.V1.voice",
        "description": "app.routers.V1.voice",
        "isExtraImport": true,
        "detail": "app.routers.V1.voice",
        "documentation": {}
    },
    {
        "label": "v1_routers",
        "importPath": "app.routers.V1",
        "description": "app.routers.V1",
        "isExtraImport": true,
        "detail": "app.routers.V1",
        "documentation": {}
    },
    {
        "label": "StaticFiles",
        "importPath": "fastapi.staticfiles",
        "description": "fastapi.staticfiles",
        "isExtraImport": true,
        "detail": "fastapi.staticfiles",
        "documentation": {}
    },
    {
        "label": "Jinja2Templates",
        "importPath": "fastapi.templating",
        "description": "fastapi.templating",
        "isExtraImport": true,
        "detail": "fastapi.templating",
        "documentation": {}
    },
    {
        "label": "CORSMiddleware",
        "importPath": "fastapi.middleware.cors",
        "description": "fastapi.middleware.cors",
        "isExtraImport": true,
        "detail": "fastapi.middleware.cors",
        "documentation": {}
    },
    {
        "label": "routes",
        "importPath": "app.routers",
        "description": "app.routers",
        "isExtraImport": true,
        "detail": "app.routers",
        "documentation": {}
    },
    {
        "label": "difflib",
        "kind": 6,
        "isExtraImport": true,
        "importPath": "difflib",
        "description": "difflib",
        "detail": "difflib",
        "documentation": {}
    },
    {
        "label": "fuzz",
        "importPath": "fuzzywuzzy",
        "description": "fuzzywuzzy",
        "isExtraImport": true,
        "detail": "fuzzywuzzy",
        "documentation": {}
    },
    {
        "label": "librosa",
        "kind": 6,
        "isExtraImport": true,
        "importPath": "librosa",
        "description": "librosa",
        "detail": "librosa",
        "documentation": {}
    },
    {
        "label": "AutoFeatureExtractor",
        "importPath": "transformers",
        "description": "transformers",
        "isExtraImport": true,
        "detail": "transformers",
        "documentation": {}
    },
    {
        "label": "Wav2Vec2BertModel",
        "importPath": "transformers",
        "description": "transformers",
        "isExtraImport": true,
        "detail": "transformers",
        "documentation": {}
    },
    {
        "label": "AutoModelForSpeechSeq2Seq",
        "importPath": "transformers",
        "description": "transformers",
        "isExtraImport": true,
        "detail": "transformers",
        "documentation": {}
    },
    {
        "label": "AutoProcessor",
        "importPath": "transformers",
        "description": "transformers",
        "isExtraImport": true,
        "detail": "transformers",
        "documentation": {}
    },
    {
        "label": "pipeline",
        "importPath": "transformers",
        "description": "transformers",
        "isExtraImport": true,
        "detail": "transformers",
        "documentation": {}
    },
    {
        "label": "soundfile",
        "kind": 6,
        "isExtraImport": true,
        "importPath": "soundfile",
        "description": "soundfile",
        "detail": "soundfile",
        "documentation": {}
    },
    {
        "label": "cosine_similarity",
        "importPath": "sklearn.metrics.pairwise",
        "description": "sklearn.metrics.pairwise",
        "isExtraImport": true,
        "detail": "sklearn.metrics.pairwise",
        "documentation": {}
    },
    {
        "label": "numpy",
        "kind": 6,
        "isExtraImport": true,
        "importPath": "numpy",
        "description": "numpy",
        "detail": "numpy",
        "documentation": {}
    },
    {
        "label": "torch",
        "kind": 6,
        "isExtraImport": true,
        "importPath": "torch",
        "description": "torch",
        "detail": "torch",
        "documentation": {}
    },
    {
        "label": "load_dataset",
        "importPath": "datasets",
        "description": "datasets",
        "isExtraImport": true,
        "detail": "datasets",
        "documentation": {}
    },
    {
        "label": "annotations",
        "importPath": "__future__",
        "description": "__future__",
        "isExtraImport": true,
        "detail": "__future__",
        "documentation": {}
    },
    {
        "label": "site",
        "kind": 6,
        "isExtraImport": true,
        "importPath": "site",
        "description": "site",
        "detail": "site",
        "documentation": {}
    },
    {
        "label": "sys",
        "kind": 6,
        "isExtraImport": true,
        "importPath": "sys",
        "description": "sys",
        "detail": "sys",
        "documentation": {}
    },
    {
        "label": "router",
        "kind": 5,
        "importPath": "app.routers.V1.voice.voice_router",
        "description": "app.routers.V1.voice.voice_router",
        "peekOfCode": "router = APIRouter(prefix=\"/voice\", tags=[\"Voice\"])\[email protected](\"/transcribe\")\nasync def transcribe_audio(\n    file: Annotated[UploadFile, File()], matcher_text: Annotated[str, Body()]\n):\n    try:\n        # Validate file type\n        if not file.filename.endswith(\".wav\"):\n            raise HTTPException(\n                status_code=status.HTTP_400_BAD_REQUEST,",
        "detail": "app.routers.V1.voice.voice_router",
        "documentation": {}
    },
    {
        "label": "router",
        "kind": 5,
        "importPath": "app.routers.V1.v1_routers",
        "description": "app.routers.V1.v1_routers",
        "peekOfCode": "router = APIRouter()\n\"\"\" include auth routes \"\"\"\nrouter.include_router(voice_router.router)",
        "detail": "app.routers.V1.v1_routers",
        "documentation": {}
    },
    {
        "label": "router",
        "kind": 5,
        "importPath": "app.routers.routes",
        "description": "app.routers.routes",
        "peekOfCode": "router = APIRouter()\n\"\"\" include the v1 routes here \"\"\"\nrouter.include_router(v1_routers.router)",
        "detail": "app.routers.routes",
        "documentation": {}
    },
    {
        "label": "app",
        "kind": 5,
        "importPath": "app.main",
        "description": "app.main",
        "peekOfCode": "app = FastAPI(\n    title=\"Mother Tongue Voice Matcher\",\n    version=\"0.0.5\",\n    servers=[{\n        \"url\": \"http://127.0.0.1:8000/api/v1\", \"description\": \"Local Server\"\n    }],\n    root_path=\"/api/v1\",\n    root_path_in_servers=False,\n)\n# cors policy",
        "detail": "app.main",
        "documentation": {}
    },
    {
        "label": "origins",
        "kind": 5,
        "importPath": "app.main",
        "description": "app.main",
        "peekOfCode": "origins = [\n    \"http://localhost\",\n    \"http://localhost:8080\",\n    \"http://localhost:3000\",\n    \"http://localhost:5173\",\n    \"http://127.0.0.1\",\n    \"http://127.0.0.1:8080\",\n    \"http://127.0.0.1:3000\",\n    \"http://127.0.0.1:5173\",\n]",
        "detail": "app.main",
        "documentation": {}
    },
    {
        "label": "templates",
        "kind": 5,
        "importPath": "app.main",
        "description": "app.main",
        "peekOfCode": "templates = Jinja2Templates(directory=\"app/templates\")\[email protected](\"/\", response_class=HTMLResponse, include_in_schema=False)\nasync def root(request: Request):\n    \"\"\"set the root to show a html welcome page\"\"\"\n    return templates.TemplateResponse(request=request, name=\"index.html\")\n# include all the other api endpoints\napp.include_router(routes.router)",
        "detail": "app.main",
        "documentation": {}
    },
    {
        "label": "phonetic_match",
        "kind": 2,
        "importPath": "app.matcher",
        "description": "app.matcher",
        "peekOfCode": "def phonetic_match(word1, word2):\n    \"\"\"\n    Compares two words based on their phonetic similarity.\n    \"\"\"\n    return fuzz.ratio(word1, word2)\n# Custom sequence matching function\ndef sequence_match(a, b):\n    \"\"\"\n    Uses sequence matching to compare two sequences of words.\n    \"\"\"",
        "detail": "app.matcher",
        "documentation": {}
    },
    {
        "label": "sequence_match",
        "kind": 2,
        "importPath": "app.matcher",
        "description": "app.matcher",
        "peekOfCode": "def sequence_match(a, b):\n    \"\"\"\n    Uses sequence matching to compare two sequences of words.\n    \"\"\"\n    return difflib.SequenceMatcher(None, a, b).ratio()\n# Main function to compare texts with percentage match\ndef compare_texts(text1, text2):\n    \"\"\"\n    Compares two texts using phonetic matching and sequence matching,\n    returning a percentage match score.",
        "detail": "app.matcher",
        "documentation": {}
    },
    {
        "label": "compare_texts",
        "kind": 2,
        "importPath": "app.matcher",
        "description": "app.matcher",
        "peekOfCode": "def compare_texts(text1, text2):\n    \"\"\"\n    Compares two texts using phonetic matching and sequence matching,\n    returning a percentage match score.\n    \"\"\"\n    words1 = text1.lower().split()\n    words2 = text2.lower().split()\n    total_matches = len(words1)\n    mismatches = 0\n    for word1, word2 in zip(words1, words2):",
        "detail": "app.matcher",
        "documentation": {}
    },
    {
        "label": "match",
        "kind": 2,
        "importPath": "app.matcher",
        "description": "app.matcher",
        "peekOfCode": "def match(original, transcription):\n    return compare_texts(original, transcription)",
        "detail": "app.matcher",
        "documentation": {}
    },
    {
        "label": "load_and_resample_audio",
        "kind": 2,
        "importPath": "app.mfcc",
        "description": "app.mfcc",
        "peekOfCode": "def load_and_resample_audio(file_path, target_sample_rate=16000):\n    audio_input, sample_rate = sf.read(file_path)\n    if sample_rate != target_sample_rate:\n        audio_input = librosa.resample(\n            audio_input, orig_sr=sample_rate, target_sr=target_sample_rate\n        )\n    return audio_input, sample_rate\ndef calculate_mfcc(audio_data, sample_rate):\n    mfccs = librosa.feature.mfcc(y=audio_data, sr=sample_rate)\n    mfccs_scaled = np.mean(mfccs.T, axis=0)  # Average across time dimension",
        "detail": "app.mfcc",
        "documentation": {}
    },
    {
        "label": "calculate_mfcc",
        "kind": 2,
        "importPath": "app.mfcc",
        "description": "app.mfcc",
        "peekOfCode": "def calculate_mfcc(audio_data, sample_rate):\n    mfccs = librosa.feature.mfcc(y=audio_data, sr=sample_rate)\n    mfccs_scaled = np.mean(mfccs.T, axis=0)  # Average across time dimension\n    return mfccs_scaled\ndef calculate_similarity(mfccs1, mfccs2):\n    similarity = cosine_similarity(\n        mfccs1.reshape(1, -1), mfccs2.reshape(1, -1))\n    return similarity[0][0]\ndef mfcc_similarty_check(original: str, recorded: str):\n    correct_pronunciation_audio, _ = load_and_resample_audio(original)",
        "detail": "app.mfcc",
        "documentation": {}
    },
    {
        "label": "calculate_similarity",
        "kind": 2,
        "importPath": "app.mfcc",
        "description": "app.mfcc",
        "peekOfCode": "def calculate_similarity(mfccs1, mfccs2):\n    similarity = cosine_similarity(\n        mfccs1.reshape(1, -1), mfccs2.reshape(1, -1))\n    return similarity[0][0]\ndef mfcc_similarty_check(original: str, recorded: str):\n    correct_pronunciation_audio, _ = load_and_resample_audio(original)\n    user_pronunciation_audio, sample_rate = load_and_resample_audio(recorded)\n    # Extract MFCCs from audio data\n    correct_mfccs = calculate_mfcc(correct_pronunciation_audio, sample_rate)\n    user_mfccs = calculate_mfcc(user_pronunciation_audio, sample_rate)",
        "detail": "app.mfcc",
        "documentation": {}
    },
    {
        "label": "mfcc_similarty_check",
        "kind": 2,
        "importPath": "app.mfcc",
        "description": "app.mfcc",
        "peekOfCode": "def mfcc_similarty_check(original: str, recorded: str):\n    correct_pronunciation_audio, _ = load_and_resample_audio(original)\n    user_pronunciation_audio, sample_rate = load_and_resample_audio(recorded)\n    # Extract MFCCs from audio data\n    correct_mfccs = calculate_mfcc(correct_pronunciation_audio, sample_rate)\n    user_mfccs = calculate_mfcc(user_pronunciation_audio, sample_rate)\n    distance = np.linalg.norm(correct_mfccs.flatten() - user_mfccs.flatten())\n    # Calculate cosine similarity using MFCCs\n    similarity_score = calculate_similarity(correct_mfccs, user_mfccs)\n    accuracy_percentage = similarity_score * 100",
        "detail": "app.mfcc",
        "documentation": {}
    },
    {
        "label": "model_id",
        "kind": 5,
        "importPath": "app.mfcc",
        "description": "app.mfcc",
        "peekOfCode": "model_id = \"facebook/w2v-bert-2.0\"\nfeature_extractor = AutoFeatureExtractor.from_pretrained(model_id)\nmodel = Wav2Vec2BertModel.from_pretrained(model_id)\ndef load_and_resample_audio(file_path, target_sample_rate=16000):\n    audio_input, sample_rate = sf.read(file_path)\n    if sample_rate != target_sample_rate:\n        audio_input = librosa.resample(\n            audio_input, orig_sr=sample_rate, target_sr=target_sample_rate\n        )\n    return audio_input, sample_rate",
        "detail": "app.mfcc",
        "documentation": {}
    },
    {
        "label": "feature_extractor",
        "kind": 5,
        "importPath": "app.mfcc",
        "description": "app.mfcc",
        "peekOfCode": "feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)\nmodel = Wav2Vec2BertModel.from_pretrained(model_id)\ndef load_and_resample_audio(file_path, target_sample_rate=16000):\n    audio_input, sample_rate = sf.read(file_path)\n    if sample_rate != target_sample_rate:\n        audio_input = librosa.resample(\n            audio_input, orig_sr=sample_rate, target_sr=target_sample_rate\n        )\n    return audio_input, sample_rate\ndef calculate_mfcc(audio_data, sample_rate):",
        "detail": "app.mfcc",
        "documentation": {}
    },
    {
        "label": "model",
        "kind": 5,
        "importPath": "app.mfcc",
        "description": "app.mfcc",
        "peekOfCode": "model = Wav2Vec2BertModel.from_pretrained(model_id)\ndef load_and_resample_audio(file_path, target_sample_rate=16000):\n    audio_input, sample_rate = sf.read(file_path)\n    if sample_rate != target_sample_rate:\n        audio_input = librosa.resample(\n            audio_input, orig_sr=sample_rate, target_sr=target_sample_rate\n        )\n    return audio_input, sample_rate\ndef calculate_mfcc(audio_data, sample_rate):\n    mfccs = librosa.feature.mfcc(y=audio_data, sr=sample_rate)",
        "detail": "app.mfcc",
        "documentation": {}
    },
    {
        "label": "get_transcription",
        "kind": 2,
        "importPath": "app.transcriber",
        "description": "app.transcriber",
        "peekOfCode": "def get_transcription(file: str):\n    result = pipe(file, generate_kwargs={\"language\": \"shona\"})\n    return result[\"text\"]",
        "detail": "app.transcriber",
        "documentation": {}
    },
    {
        "label": "device",
        "kind": 5,
        "importPath": "app.transcriber",
        "description": "app.transcriber",
        "peekOfCode": "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\ntorch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32\nmodel_id = \"openai/whisper-large-v3\"\nmodel = AutoModelForSpeechSeq2Seq.from_pretrained(\n    model_id,\n    torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True\n)\nmodel.to(device)\nprocessor = AutoProcessor.from_pretrained(model_id)\npipe = pipeline(",
        "detail": "app.transcriber",
        "documentation": {}
    },
    {
        "label": "torch_dtype",
        "kind": 5,
        "importPath": "app.transcriber",
        "description": "app.transcriber",
        "peekOfCode": "torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32\nmodel_id = \"openai/whisper-large-v3\"\nmodel = AutoModelForSpeechSeq2Seq.from_pretrained(\n    model_id,\n    torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True\n)\nmodel.to(device)\nprocessor = AutoProcessor.from_pretrained(model_id)\npipe = pipeline(\n    \"automatic-speech-recognition\",",
        "detail": "app.transcriber",
        "documentation": {}
    },
    {
        "label": "model_id",
        "kind": 5,
        "importPath": "app.transcriber",
        "description": "app.transcriber",
        "peekOfCode": "model_id = \"openai/whisper-large-v3\"\nmodel = AutoModelForSpeechSeq2Seq.from_pretrained(\n    model_id,\n    torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True\n)\nmodel.to(device)\nprocessor = AutoProcessor.from_pretrained(model_id)\npipe = pipeline(\n    \"automatic-speech-recognition\",\n    model=model,",
        "detail": "app.transcriber",
        "documentation": {}
    },
    {
        "label": "model",
        "kind": 5,
        "importPath": "app.transcriber",
        "description": "app.transcriber",
        "peekOfCode": "model = AutoModelForSpeechSeq2Seq.from_pretrained(\n    model_id,\n    torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True\n)\nmodel.to(device)\nprocessor = AutoProcessor.from_pretrained(model_id)\npipe = pipeline(\n    \"automatic-speech-recognition\",\n    model=model,\n    tokenizer=processor.tokenizer,",
        "detail": "app.transcriber",
        "documentation": {}
    },
    {
        "label": "processor",
        "kind": 5,
        "importPath": "app.transcriber",
        "description": "app.transcriber",
        "peekOfCode": "processor = AutoProcessor.from_pretrained(model_id)\npipe = pipeline(\n    \"automatic-speech-recognition\",\n    model=model,\n    tokenizer=processor.tokenizer,\n    feature_extractor=processor.feature_extractor,\n    max_new_tokens=128,\n    chunk_length_s=30,\n    batch_size=16,\n    return_timestamps=True,",
        "detail": "app.transcriber",
        "documentation": {}
    },
    {
        "label": "pipe",
        "kind": 5,
        "importPath": "app.transcriber",
        "description": "app.transcriber",
        "peekOfCode": "pipe = pipeline(\n    \"automatic-speech-recognition\",\n    model=model,\n    tokenizer=processor.tokenizer,\n    feature_extractor=processor.feature_extractor,\n    max_new_tokens=128,\n    chunk_length_s=30,\n    batch_size=16,\n    return_timestamps=True,\n    torch_dtype=torch_dtype,",
        "detail": "app.transcriber",
        "documentation": {}
    },
    {
        "label": "dataset",
        "kind": 5,
        "importPath": "app.transcriber",
        "description": "app.transcriber",
        "peekOfCode": "dataset = load_dataset(\n    \"distil-whisper/librispeech_long\", \"clean\", split=\"validation\")\nsample = dataset[0][\"audio\"]\ndef get_transcription(file: str):\n    result = pipe(file, generate_kwargs={\"language\": \"shona\"})\n    return result[\"text\"]",
        "detail": "app.transcriber",
        "documentation": {}
    },
    {
        "label": "sample",
        "kind": 5,
        "importPath": "app.transcriber",
        "description": "app.transcriber",
        "peekOfCode": "sample = dataset[0][\"audio\"]\ndef get_transcription(file: str):\n    result = pipe(file, generate_kwargs={\"language\": \"shona\"})\n    return result[\"text\"]",
        "detail": "app.transcriber",
        "documentation": {}
    },
    {
        "label": "bin_dir",
        "kind": 5,
        "importPath": "env.Scripts.activate_this",
        "description": "env.Scripts.activate_this",
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]