Initial version

.gitattributes

@@ -23,5 +23,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,8 +1,8 @@
 ---
 title: Voice Queries
-emoji: 💩
-colorFrom: pink
-colorTo: indigo
+emoji: 🐢
+colorFrom: green
+colorTo: yellow
 sdk: gradio
 app_file: app.py
 pinned: false

app.py

@@ -0,0 +1,110 @@
+import nltk
+import pickle
+import pandas as pd
+import gradio as gr
+import numpy as np
+from sentence_transformers import SentenceTransformer, util
+from transformers import pipeline
+from librosa import load, resample
+
+# Constants
+filename = 'df10k_SP500_2020.csv.zip'
+
+model_name = 'sentence-transformers/msmarco-distilbert-base-v4'
+max_sequence_length = 512
+embeddings_filename = 'df10k_embeddings_msmarco-distilbert-base-v4.npz'
+asr_model = 'facebook/wav2vec2-xls-r-300m-21-to-en'
+
+# Load corpus
+df = pd.read_csv(filename)
+df.drop_duplicates(inplace=True)
+print(f'Number of documents: {len(df)}')
+
+corpus = []
+sentence_count = []
+for _, row in df.iterrows():
+    # We're interested in the 'mdna' column: 'Management discussion and analysis'
+    sentences = nltk.tokenize.sent_tokenize(str(row['mdna']), language='english')
+    sentence_count.append(len(sentences))
+    for _,s in enumerate(sentences):
+        corpus.append(s)
+print(f'Number of sentences: {len(corpus)}')
+
+# Load pre-embedded corpus
+corpus_embeddings = np.load(embeddings_filename)['arr_0']
+print(f'Number of embeddings: {corpus_embeddings.shape[0]}')
+
+# Load embedding model
+model = SentenceTransformer(model_name)
+model.max_seq_length = max_sequence_length
+
+# Load speech to text model
+asr = pipeline('automatic-speech-recognition', model=asr_model, feature_extractor=asr_model)
+
+def find_sentences(query, hits):
+    query_embedding = model.encode(query)
+    hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=hits)
+    hits = hits[0]
+
+    output = pd.DataFrame(columns=['Ticker', 'Form type', 'Filing date', 'Text', 'Score'])
+    for hit in hits:
+        corpus_id = hit['corpus_id']
+        # Find source document based on sentence index
+        count = 0
+        for idx, c in enumerate(sentence_count):
+            count+=c
+            if (corpus_id > count-1):
+                continue
+            else:
+                doc = df.iloc[idx]
+                new_row = { 
+                	'Ticker'     : doc['ticker'], 
+                	'Form type'  : doc['form_type'],
+                	'Filing date': doc['filing_date'], 
+                	'Text'       : corpus[corpus_id],
+                	'Score'      : '{:.2f}'.format(hit['score'])
+                }
+                output = output.append(new_row, ignore_index=True)
+                break
+    return output
+
+
+def process(input_selection, query, filepath, hits):
+	if input_selection=='speech':
+		speech, sampling_rate = load(filepath)
+		if sampling_rate != 16000:
+			speech = resample(speech, sampling_rate, 16000)
+		text = asr(speech)['text']
+	else:
+		text = query
+	return text, find_sentences(text, hits)
+
+# Gradio inputs
+buttons    = gr.inputs.Radio(['text','speech'], type='value', default='speech', label='Input selection')
+text_query = gr.inputs.Textbox(lines=1, label='Text input', default='The company is under investigation by tax authorities for potential fraud.')
+mic        = gr.inputs.Audio(source='microphone', type='filepath', label='Speech input', optional=True)
+slider     = gr.inputs.Slider(minimum=1, maximum=10, step=1, default=3, label='Number of hits')
+
+# Gradio outputs
+speech_query = gr.outputs.Textbox(type='auto', label='Query string')
+results      = gr.outputs.Dataframe(
+				headers=['Ticker', 'Form type', 'Filing date', 'Text', 'Score'], 
+				label='Query results')
+
+iface = gr.Interface(
+	theme='huggingface',
+	description='This Spaces lets you query a text corpus containing 2020 annual filings for all S&P500 companies. You can type a text query in English, or record an audio query in 21 languages.',
+	fn=process,
+	inputs=[buttons,text_query,mic,slider], 
+	outputs=[speech_query, results],
+	examples=[
+		['text', "The company is under investigation by tax authorities for potential fraud.", 'dummy.wav', 3],
+		['text', "How much money does Microsoft make with Azure?", 'dummy.wav', 3],
+        ['speech', "Nos ventes internationales ont significativement augmenté.", 'sales_16k_fr.wav', 3],
+        ['speech', "Le prix de l'énergie pourrait avoir un impact négatif dans le futur.", 'energy_16k_fr.wav', 3],
+        ['speech', "El precio de la energía podría tener un impacto negativo en el futuro.", 'energy_24k_es.wav', 3],
+        ['speech', "Mehrere Steuerbehörden untersuchen unser Unternehmen.", 'tax_24k_de.wav', 3]
+    ],
+    allow_flagging=False
+)
+iface.launch()

df10k_SP500_2020.csv.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:984d7a036dba3c32e176e6609392909b005bc5ac030de24427f0982c88aaaf0d
+size 134796242

df10k_embeddings_msmarco-distilbert-base-v4.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa084a851fd82187d2e0aa1ab72fb4e1ac127a07cbcc3597a551719850b8b25d
+size 526747035

requirements.txt

@@ -0,0 +1,7 @@
+torch
+transformers
+nltk
+pandas
+numpy
+sentence-transformers
+librosa