spacy segfault

def readDoc(path, r):
    report = []
    fn = os.path.join(path, r) if ".txt" in r else os.path.join(path, r + ".txt")

    for l in open(fn, encoding="utf-8").readlines():
        if l == "\n":
            report.append(l)
        else:
            report.append(l.strip())
    return " ".join(report).replace("  ", " ")



def getFilesDir(path):
    return [f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]



@plac.annotations(
        start=plac.Annotation("Amount to start", "option", "s", int),
        interval=plac.Annotation("Interval for document increase", "option", "n", int),
        iterations=plac.Annotation("Number of iterations to run", "option", "i", int, metavar="i"), docs=plac.Annotation("Number of documents to test", "option", "d", int),
        reload_after_iteration=plac.Annotation("Reload model after iteration", "option", "r", bool), dont_repeat=plac.Annotation("Dont reuse same batch", "option", "b", bool),
        garbage_collect=plac.Annotation("Run gc after each iteration", "option", "g", bool),

)
def main(start=500, interval=250, iterations=10, docs=3000, reload_after_iteration=False, dont_repeat=False, garbage_collect=False):
    print("Reading Files")
    if dont_repeat and start + interval * iterations > docs:
        docs = start + interval + iterations
    documents = [readDoc(DOC_PATH, f) for f in getFilesDir(DOC_PATH)[:docs]]
    print("Finished reading files")
    # dump_path = os.path.join(os.getcwd(),"seg_dumps")
    # if not os.path.isdir(dump_path): os.mkdir(dump_path)
    current_amount = start
    previous = 0
    nlp = spacy.load("en_core_web_sm")
    for i in range(iterations):

        print("Iteration {}, {} documents".format(i,current_amount))
        if current_amount > docs:
            print("{} is more than {}".format(current_amount, docs))
            break
        if dont_repeat:
            docs_use = documents[previous:current_amount]
        else:
            docs_use = documents[:current_amount]
        t0 = datetime.utcnow()
        parsed_docs = list(nlp.pipe(docs_use))
        t1 = datetime.utcnow()
        time_passed = t1 - t0
        minutes, seconds = divmod(time_passed.seconds, 60)
        print("Parsed {} in {}m {}s".format(current_amount - previous,minutes, seconds))
        if dont_repeat:
            previous = current_amount
        if reload_after_iteration:
            print("Reloading nlp")
            del nlp
            nlp = spacy.load("en_core_web_sm")
        if garbage_collect:
            gc.collect()
        current_amount += interval


if __name__ == "__main__":
    print("Starting")
    plac.call(main)